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81st EAGE Conference and Exhibition 2019 Workshop Programme
- Conference date: June 3-6, 2019
- Location: London, UK
- Published: 03 June 2019
93 results
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A Brief Overview on Seismic Attenuation
More LessSummarySeismic waves decay due to geometrical spreading (in 2D and 3D) and scattering (energy is conserved), and anelastic -- or intrinsic -- attenuation (energy is lost to heat). Amplitude decay in the last two cases is accompanied with wave-velocity dispersion, by which each Fourier component of the signal travels with a different phase velocity (Kramers-Kronig relations). Attenuation can be described by a phenomenological (non-predictive) theory, as the Burgers mechanical model -- composed of springs and dashpots --, or with predictive models, such as the scattering theory, and the Biot and related models of poroelasticity (wave-induced fluid-flow attenuation). Another phenomenological approach is the use of temporal or spatial fractional derivatives, e.g., Kjartansson and Cole-Cole models. In the following, I present a brief overview on the various attenuation mechanisms, where most of the material refers to Carcione (2014) .
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Seismic Attenuation Mechanisms in Fractured Fluid Saturated Media – Numerical and Field Examples
Authors E. Caspari, N.D. Barbosa, M. Novikov, V. Lisitsa, J. Hunziker, B. Quintal, G. Rubino and K. HolligerSummaryA number of different mechanisms can cause attenuation of propagating seismic waves in a fractured fluid-saturated porous medium, notably geometrical spreading, displacement of pore fluid relative to the solid frame, and transmission losses and scattering. In this study, we examine these attenuation mechanisms using numerical forward simulations and a field example. The numerical methods include a quasi-static upscaling approach and wave propagation simulations. They are based on Biot's equations of poroelasticity and, hence, fractures are modeled as soft, highly porous and permeable features. The field examples include full-waveform sonic data from the Grimsel Test Site underground laboratory situated in a granodioritic rock mass, which exhibits both brittle and ductile deformation structures at various scales.
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Use of the General Fractional Zener Model to Represent Attenuation in Poro-Viscoelastic Numerical Modeling
Authors X. Liu and S. GreenhalghSummaryTo approximate seismic wave propagation in double porosity media we use the governing equations of effective Biot theory with complex phase velocity and attenuation dispersion characteristics. To upscale them and extend to shear waves we use the poro-viscoelastic modeling approach of multiple fractional Zener elements and apply a frequency-space domain mixed grid finite difference simulation method to calculate wavefields for solid particle velocity, fluid flux and pore pressure.
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Dispersion and Attenuation of Wave Velocity in Fluid-Saturated Rocks : Experimental Investigations
Authors J. Fortin, J. Borgomano and S. ChapmanSummaryFor fluid-saturated rocks, comparing ultrasonic measurements (1 MHz) in the laboratory and seismic (100 Hz) or logging (10 kHz) measurements in the field is not straightforward due to dispersion of the elastic-wave velocities. If there are several theoretical models, there is a lack of data. We developed an apparatus for measurements over a large-frequency range, by the combination of forced oscillations (0.004 to 100 kHz in apparent frequency) and ultrasonic measurements (1 MHz) at various effective pressures. Our experimental results typically show two cut-off frequencies: i) one related to a drained/undrained transition; and ii) one related to squirt flow.
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Attenuation From Passive Seismic Monitoring
More LessSummaryMeasurements of attenuation from passive seismic monitoring datasets increase in number. We provide an overview of methods that are used to determine attenuation factors using detected microseismic events. We discuss applications and advantages of using passive monitoring to characterize medium -in particular hydrocarbon reservoirs.
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Q Compensation: From Nice to Have to Mandatory But…
Authors P. Charron, B. Duquet, C. Agut and A. LaframSummaryAttenuation is a well-known phenomenon in seismic. For tens of years, this has not been compensated or partially compensated with an inaccuracy that would not be accepted for any other processing steps. Most often, only a phase de-absorption with a constant Q value was applied and the amplitude absorption was compensated with spectral enhancement. This was considered as acceptable until today since the frequency bandwidth was limited to around 2 octaves and the effect of this de-absorption was not clearly observed as an improvement or adrawback. Today, the market has moved forward and the broadband seismic has become a standard. The consequence is that the bandwidth may now be spread over up to 5 octaves depending on the area and the burial of investigation. This wider frequency bandwidth is naturally unbalanced according to the magnitude of the absorption. In order to deliver the promises of the broadband (resolution), compensating the absorption is not an option anymore. It has become a processing step of a major importance that will impact the resolution, the phase and the overall “aspect” of the final result that the end-user will interpret or use to extract some attribute through AVO analysis or inversion processes.
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Q-Tomography: Status and Challenges
Authors Fatiha Gamar-Sadat, Alessandro Pintus, Patrice Guillaume and Andrew WrightSummaryAlmost all current time or depth seismic studies need to go through a correction process to recover energy lost by absorption phenomenon. The so-called Q factor is responsible for dissipation of high-frequency seismic energy, which decreases seismic amplitudes and causes velocity dispersion. For general background Q, a post-migration inverse Q filtering ( Wang, 2002 ), using smooth or even constant Q, may be sufficient for data with gentle geology. In areas with more absorptive heterogeneities such as unconsolidated materialor gas, theneed for a morecomplex Q model is necessary for an accurate correction. Brzostowski and McMechan (1992) have been pioneers for addressing this problem, adapting Q-Tomography from fundamental to applied seismology. Over the last decade, it has resulted in an industrial solution ( Cavalca et al., 2011 ; Valenciano and Chemingui, 2013 ; xin et al., 2014 ; Gamar et al., 2015 ) using VSP or surface seismic data.
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Viscoelastic Full Waveform Inversion of Wide-Angle Data: Application to Ultra-Long Offset Data from Mentawai Basin, Indonesia
More LessSummaryFull Waveform Inversion (FWI) is a powerful tool to quantify the Earth's subsurface structure. However, most of the FWI applications have been limited to acoustic media. In geological settings, such as gas clouds, gas sand, melt lens, where the attenuation becomes important, one must use a viscoelastic FWI. Here we present the theory and application of a viscoelastic FWI in the time domain. First, we carried out sensitivity analyses for back-scattered, reflected and transmitted waves. We find that the presence of attenuation has a significant effect on post-critical reflections, but it has little or no effect on near-offset reflection data, suggesting that the inversion of port-critical reflections can help to reduce the cross talks between attenuation and velocity contrast or propagation effects. We have first tested the method on synthetic data and then applied to 15 km long offset data acquired by CGG Offshore central Sumatra, Indonesia. Apart from the recovery of attenuation parameters, the viscoelastic inversion provides sharper P-wave velocity image as compared to the elastic FWI.
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Effect and Reconstruction of Attenuation in Acoustic FWI - Method and Field Data Application
Authors N. Kamath, R. Brossier, L. Metivier and P. YangSummaryOur contribution is divided into two parts: in the first we discuss the mechanism (based on generalised Zener body) used to incorporate attenuation into our forward modelling engine, and the manner we manage the efficient building of the FWI gradient in 3D. The second part deals with the application of mono- and multi-parameter (velocity only in visco-acoustic media, and velocity-attenuation joint inversion, respectively) FWI to a 3D OBC dataset from the Valhall field.
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A Hybrid Inversion Strategy for Visco-Acoustic Full Waveform Inversion: Application to the Marmousi Model
Authors H. Jiang and H. ChaurisSummaryVisco-acoustic full waveform inversion aims at retrieving the velocity and attenuation models, but suffers from cross-talks between parameters. Attenuation dispersion leads to equivalent kinematic velocity models, as different combinations of velocity and attenuation have the same kinematic effects for band-limited seismic waves. We propose a hybrid inversion strategy: we incorporate the kinematic relationship to guide the non-linear inversion. The hybrid inversion strategy includes two steps. It first updates the kinematic velocity, and then retrieves the velocity and attenuation models for a fixed kinematic velocity. This hybrid inversion strategy is tested on the Marmousi model dominated by reflections, and compared with the conventional simultaneous inversion strategy. It proves that the hybrid inversion strategy mitigates the cross-talks without involving the Hessian matrix.
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Joint Estimation of Velocity and Attenuation by Frequency-Domain TV-Regularized Wavefield Reconstruction Inversion
Authors H. Aghamiry, A. Gholami and S. OpertoSummaryFull waveform inversion (FWI) is a nonlinear waveform matching procedure which can provide high-resolution subsurface models. However, viscous effects must be taken into account in attenuating media to exploit the full potential of FWI. In the frequency domain, attenuation is implemented in the time-harmonic wave equation with complex-valued velocities. During the inverse problem, the real and imaginary parts of the velocity are generally processed as two independentreal-valued parameters. In this study, we process instead the velocityas a complex-valued parameter using derivative of real functions of complex variables. Moreover, we implement visco-acoustic frequency FWI with search space extension in the framework of the wavefield reconstruction inversion (WRI) method. We implement WRI with the alternating-direction method of multiplier (ADMM), which makes the parameter-estimation subproblem linear thanks to the bilinearity of the wave equation and provides a suitable framework to cascade nonsmooth regularizations and bound constraints in the objective function. In this study, we review ADMM-based WRI for complex-valued parameters and show preliminary results of joint velocity and attenuation reconstruction when inversion is performed without and with total variation (TV) regularization. We show the key role of TV regularization to decrease the ill-posedness of the velocity-attenuation reconstruction.
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Accounting for Heterogeneous Attenuation in Full-Wavefield Inversion
Authors M. Lacasse, H. Denli, L. White, V. Gudipati, S. Lee and S. TanSummaryThis talk presents a numerical approach for including attenuation in forward seismic viscoacoustic simulators in the time domain using a generalized Maxwell body (GMB). We discuss how to select the proper number of relaxation mechanisms and the values of the parameters of the GMB. We also present a few synthetic case studies determining the feasibility of performing full-wavefield inversion of viscoacoustic media, and the aperture requirements. Finally, field examples are shown where attenuation needs to be accounted for in order to perform a successful inversion.
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Integrated Visco-Acoustic Model Building: a Case Study Illustrating the Challenges and Benefits for Large Scale Exploration
Authors T. Martin, M. Barbaray, G. Venfield and V. ChavdaSummaryThe seismic character of Early and Late Cretaceous plays in deep water Côte d'Ivoire data are affected by Late Cretaceous and Paleocene channel and canyon systems. Unresolved, these create structural uncertainty and impacting amplitude fidelity. Using an integrated visco-acoustic model building sequence we resolve the impact of complex Late Cretaceous and Paleocene channel systems on deeper targets. A full wavefield FWI approach creates an accurate velocity model removing the structural uncertainty when used in the imaging step. Viscoacoustic effects were determined using tomography. Fully integrated into the sequence, this method calculated measures of log spectral ratio in demigrated space, mitigating stretch and tuning effects. The resulting combination of the complete visco-acoustic model building flow compensated for the structural complexity of the area, whilst significantly improving the amplitude fidelity of the dataset.
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Seismic Inversion for Near Surface Applications and the Derivation of Geomechanical Properties
By A. FoggSummarySeismic inversion methods broadly fall in to two categories; conversion of seismic event amplitudes in to reflectivity or the analysis primarily of seismic event arrival times (and waveform shape) to derive a velocity model. These are generically referred to as Acoustic Impedance (AI) inversion and Full Waveform Inversion (FWI) respectively, the former typically working from processed seismic reflectivity data and the latter being derived during the processing phase. Both procedures have application in the characterisation of the rock properties of shallow stratigraphic sections, indeed FWI is specifically designed (and limited to) no deeper than approximately 1500m below the mudline (though this depth is dependent on seismic acquisition parameters; notably cable length, water column height and subsurface velocity). This paper will review several different approaches to AI inversion, which can be calibrated to derive rock mechanical properties, and discuss their application to the near surface. The paper will also demonstrate how FWI can yield a high resolution image of near surface velocity which improves the seismic image and thus enhances AI inversion results. Case studies will be used to demonstrate the procedures and contrast the advantages and disadvantages of different methods.
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A Request for Quantitative Seismic Solutions for Drilling Hazards Assessment
By G. WoodSummaryIn this paper, we focus on the need to provide a quantitative approach to drilling hazard assessment and the benefits such an approach would have to reducing uncertainty and risk while potentially releasing marine real estate for drilling or developments that has previously been considered unuseable
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Towards QI for Site Investigation in Orsted
Authors K.H. Karkov and S. HviidSummaryA recently concluded Ørsted R&D project successfully demonstrated the possibility of extracting quantitative information on soil composition from 2D UHRS data by leveraging the acoustic inversion method.
Our contribution to the workshop will include an introduction to Site Investigation in Ørsted, a presentation and discussion of our identified key challenges related to acquisition, processing, calibration, interpretation and integration as well as our perceived status on these.
Examples from recent advances will be presented providing the status on commercial scale QI for site investigation purposes in Ørsted.
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Seismic Inversion for Geotechnical Problems
SummaryFor foundation design the geotechnical analyses and interpretations often rely on isolated 1D boreholes and the geophysical data is only used to confirm horizontal layering. The great amount of information capture in the geophysical data, not only related to layering but also related to soil parameters, are therefore not used. Geophysical data are collected in 2D lines and/or 3D volumes and therefore provides the natural link to re-populate geotechnical properties found in the 1D boreholes onto a larger area and thereby build a consistent and robust ground model. There is therefore a great potential in using this data in a quantitative way during all phases of a project.
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A Review of Seismic Attenuation Mechanisms, Measurements, and Inversion Strategies
By E. MorganSummaryThis segment of the Seismic Inversion for Marine Overburden Characterization workshop discusses the physical mechanisms responsible for intrinsic and scattering attenuation, rock physics models for intrinsic attenuation, common methods for measuring attenuation from seismic reflection surveys, and inversion strategies to estimate soil and fluid properties from attenuation measurements.
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Using Vs Measurements in Shallow Marine Sediment to Guide Vp Matrix Velocity Assessment for Seismic Inversion
By A. FoleySummaryMeasuring seabed velocities by transmission surveying, as opposed to those measured by reflection surveys, gives key, unambiguous results. By determination of Vs, which only responds to the sediment matrix, a more useful value of Vp can be found. Geotechnical engineers refer to this as the “drained” velocity. This value for Vp allows practitioners to calibrate inversion results and derive meaningful values for several engineering parameters of seabed soils.
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A Deep Learning Approach to Quantitatively Characterising the Marine Near-Surface
Authors M. Vardy and T. DarnellSummaryIn this paper, we present a Deep Learning workflow developed specifically for inverting marine site investigation data, comparing and contrasting it against the results obtained using traditional stochastic inversion algorithms with both synthetic and field data. In particular, we assess its potential for rapidly deriving a range of subsurface parameterisations, including geotechnical engineering properties of direct interest for various site investigation problems.
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Porosity Prediction from Shallow Subsurface Seismic Investigation - A Rock Physical Model Approach
Authors Guillaume Sauvin, Maarten Vanneste, Park Joonsang and Madshus ChristianSummaryIn this paper, we present a rock physical model to estimate the porosity from the seismic velocities and compare it with existing rock physical models. A parameter sensitivity analysis is also conducted and the various models are validated with lab data. We also propose a workflow to predict the porosity from seismic velocities at field scale and apply it to a case study.
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Time-Lapse Imaging of the Shallow Subsurface at Decimetre Scale Resolution
Authors M. Faggetter, M. Vardy, J. Dix, J. Bull and T. HenstockSummaryHigh-resolution seismic data provides information for many applications including offshore engineering work, where an accurate characterisation of the shallow marine subsurface is essential. However, single 3D volumes only provide a temporal snapshot and do not fully capture the highly dynamic nature of shallow water environments. Although changes at the seabed can be interpreted from repeat bathymetry, only very limited information about the substrate below. Here, we discuss the application of multiple, collocated, ultra-high-resolution (kHz-range) 3D seismic surveys as a tool to investigate changing processes in the marine subsurface. Examining data acquired with the 3D Chirp sub-bottom profiler, two case study examples will be presented. Results illustrate the capability for quantitative mapping of subsurface differences at decimetre-scale resolution using bin sizes of 0.25 cm and smaller.
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High Resolution Imaging and Quantitative Analysis of HV Cable and Pipeline Trenching in the Marine Environment
Authors Justin Dix, Mark E Vardy, Michael Faggetter, David White and Peter AllenSummaryThe life time performance of both HV cables (ORE inter -array and export cables and cross-continental shelf interconnectors) and oil and gas pipelines are limited by the physical properties of the sediment in which the cable/pipeline is buried. In the case of HV cables the burial material and burial depth have implications for heat dissipation from the cable, which in turn plays a primary role in cable rating and its lifetime operation and maintenance. For a pipeline changes in the density and strength of the overburden material can impact on buckling potential once in operation. Our current understanding of the key physical parameters of the sediment (e.g. grain size, porosity, permeability, thermal conductivity, relative density and strength) are based on in situ measurements of the ambient condition and rarely take account of physical property changes during the trenching process. We provide initial acoustic inversion results from high resolution 3D Chirp volumes from both a prototype scale, CPT calibrated, tank experiment and in situ trenched cables in a range of substrates. We shall demonstrate the potential of acoustic inversion to non-destructively quantify trench disturbance in this critical engineering scenarios.
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Sub-Surface Imaging at the Rockall Basin, Using Travel Time Tomography and Full Waveform Inversion
More LessSummaryThe geological structure beneath the Rockall basin and the nature of the crust are largely undefined because of the sill intrusion, lack of the seismic data coverage and deep well data penetration. The basaltic rocks prevent the seismic waves from travelling underneath them and make it hard to image below these high velocity layers. Here, we perform travel time tomography on long streamer data sets along a 80 km long profile to get a smooth P-wave velocity model using the first arrival travel time. The 2D seismic lines were acquired in 2013–14 using 10 km long streamers. We pick first arrival travel time from the shot gather after cleaning the data set. The velocity model obtained here, indicate the velocity from 1.6–4 km/s for the sediments and we also observe very high velocity ~ 6–7 km/s just 3 km below the sea-floor. This high velocity structure could be the lower crust pinching out at the Rockall high. This velocity model is used as a starting model for full waveform inversion (FWI) to get a higher resolution velocity structure.
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Opportunities and Challenges in Multi-Component Processing
More LessSummaryThis talk gives an overview of the opportunities for a wider use of multi-component data and the requirement for the processing which needs to be met in order to ensure that the full value of the data are realized.
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Multi-Component Processing on Land for Complimentary PP and PS Imaging and Characterisation
Authors R. Johnston and A. AyreSummaryThe use of multi-component seismic data has risen steadily since the ‘pure’ shear-wave acquisition activity onshore of the 1980s, through the converted-wave ‘revolution’ offshore in the 1990s and beyond. To benefit the most from multi-component seismic we hope for a situation where data quality of the entire elastic wavefield is similar which allows the complimentary information from compressional and shear waves to contribute together. Where either of these waves are compromised, the combined potential will necessarily be sub-optimal. To respond to Canada's Northern Alberta shallow unconventional reservoirs and provide high resolution imaging and characterisation, the industry has developed using point source and multi-component point receiver seismic. We describe the challenges addressed in processing for high resolution PP and PS imaging which deliver improved images and complimentary PP and PS attributes for characterisation. In this heavy oil unconventional reservoir setting, PS images are revealing new details about the geology not previously seen on P-wave data alone. The multi-component data have positively influenced an appraisal drilling program and subsequent pad layout, to take advantage of the geological variations and high grade the reservoir development plan.
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Extraction of Acquisition and Processing Attributes from a Time-Lapse Ocean-Bottom Seismic Elastic Finite-Difference Study
Authors R. Whitebread, P. Kristiansen and M. BranstonSummaryObserving changes of seismic amplitude and event timing over time within recorded seismic data are a well-established path to understanding the subtler changes to reservoir properties during injection and production phases of reservoir development. To understand the impact of acquisition tolerances and factors such as noise, ghost, and multiple energy on these reservoir changes, we must move away from real recorded seismic data and look at key issues in isolation with synthetic data.
We created a small four-component ocean-bottom seismic (OBS) data set using elastic finite-difference modelling where we have full control of the physical changes at the reservoir as well as free-surface effects and deviations from nominal acquisition geometry.
We chose elastic finite-difference modelling to include interface waves into PP and VZ data as well as to allow full modelling VX and VY components to infer 4D response from the subsequently derived radial component.
Our results confirm that geometry errors rapidly degrade the observed 4D response, but that the cumulative effect of geometry deviations, tidal statics, and water velocity corrections could also mask expected reservoir changes.
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Multi-Component Seismic Data Processing of a 3D 3C Dataset for Sand Filled Channels and Rock Property Identification via PP/PS Joint Inversion : A Case History
More LessSummaryMulti-component seismic data processing of a 3D 3C dataset for sand filled channels and rock property identification via PP/PS joint inversion: A case history.
In this case history, we present improvements in sand filled channel identification and associated fracture detection, including fracture density and orientation. This was achieved by performing seismic data processing of vertical component (PP) and converted wave (PS) data for a 3D 3C dataset.
A superior quality image of the P-wave using 3D 3C data will be demonstrated by the robustness of the applied processing sequence and consistency of the utilized processing and Q.C. tools. In addition, we will demonstrate in detail the converted wave measured from the horizontal component processing.
Incorporating the interpreter's geological knowledge into the processing workflow is extremely crucial where intermingling of the data processing sequence, algorithms and geological knowledge plays a major role in obtaining successful final PS data results. Integrating PP / PS images by introducing both components in one processed solution should alleviate the interpretation challenges at the target reservoir interval, due to the added precision in elastic properties derivation through the PP/PS joint inversion process.
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Multi-Component Seismic Data Processing of a 3D 4C OBC Dataset for Lithological Identification Through PP/PS Joint Inversion: A Case History
More LessSummaryIn this case history we present the advantages and improvements in lithological discrimination and identification by implementing a robust seismic data processing workflow of vertical component (PP) and converted wave (PS), for a 3D 4C dual sensor OBC dataset.
The image quality of the P-wave using dual sensor data will be demonstrated by the robustness of the applied processing sequence and quality control tools. We will demonstrate in details the converted wave measured from the horizontal component processing, where the interpreters geological knowledge, incorporated into the integration of processing applications, plays a major role in the success of the final PS data results.
Integrating PP / PS images and introducing PP, PS processed data in one solution should enhance the interpretation resolution at the target interval. Improved Vp/Vs for prominent markers and more accurate AI through PP/PS joint inversion is continuously evaluated throughout the processing workflow. Eventually, our main objective is the utilization of PP/PS processed data in joint inversion to produce accurate lithology discrimination maps.
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Anisotropy processing of multi-component seismic data – Part 1: Theory and Implementation
More LessSummaryAn oil-gas reservoir can be treated as an orthorhombic medium which can be considered as a combination of VTI and HTI media. It means, for a given azimuthal direction, that an orthorhombic medium can be treated as a VTI medium. To process the seismic reflection data acquired from such a medium (the reservoir), the common practice is to separate the processing into two stages: anisotropy processing for a VTI medium and anisotropy processing for a HTI medium. In the first stage, we can ignore the HTI features and apply VTI anisotropy processing to this data, from which we can estimate the velocity and anisotropy parameters and obtain seismic images. Actually, the results are the average results for the whole dataset. Then we use the estimated velocities and anisotropy parameters as an initial model for HTI processing. In HTI processing, we need to separate the seismic data according to the azimuthal direction of the offset. Then we can carry on VTI processing for each set of azimuthal data. The results will be dependent on the azimuthal direction. The azimuthal dependence of velocity and anisotropy parameters can be used to estimate the fracture direction and strength of anisotropy of the reservoir.
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Anisotropy Processing of Multi-Component Seismic Data – Part 2: Processing Demonstration Based on CXtools
More LessSummaryIn Part 1, I introducted the theory for anisotropic processing in VTI and HTI media and CXtools, a processing package which implements the theory. In this part, I will show how to use CXtools to carry out anisotropic processing based on a 3D multicomponent seismic dataset. During the demonstration, I also show how to adjust the velocity and anisotropy parameters to improve the results. This live demonstration is separated into three sessions:
- How to run the CXtools
- Anisotropic processing for VTI media
- Anisotropic processing for HTI media
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Anisotropy Processing of Multi-Component Seismic Data – Part 3: Pre-Stack Time Migration
More LessSummaryPrestack time migration is an efficient imaging method for processing seismic data due to its input/output flexibility and target-orientation, and has recently become a routine step in the seismic data processing flow. Prestack Kirchhoff time migration can produce high-quality migrated images from real seismic data with the correct velocity models. Important issues in applying prestack time migration are how to obtain the correct velocity model, and selecting suitable travel time formulae for different wave types (PP and PS) in different media. In this part, I will introduce the theory for prestack time migration in a VTI medium, and a method for obtaining the optimal velocity model for prestack time migration from seismic data.
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How High Can We Go? Pushing Limits of Lateral and Vertical Resolution in Deepwater Seismic
Authors P. Hatchell, P. Dutta, S. Bakku and Z. YangSummaryWe acquired low-cost high-resolution 3D (HR3D) streamer seismic surveys using the P-Cable system with a small air-gun source array (300 in3) over deepwater fields with water depths ranging from 900m to 3000 m. The P-Cable HR3D streamer system employs multiple short streamers (100 m) connected to a cross-cable. In our surveys we deployed 18 streamer cables with each of the 100 m streamer cables having 16 hydrophone groups spaced at 6.25 m intervals. With shot intervals spaced every 12.5 m, the nominal bin size of this configuration is 6.25 m × 3.125 m and the fold is four. In the Shallow sections, we achieved migrated images with frequencies up to 200 Hz. The lateral resolution of the images is found to be superior to that from high-resolution processing of conventional data (streamer and OBN). In the deeper section, the frequencies dropped to much lower values indicating that the earth has more control on the high frequency end than the geophysicist.
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Imaging with Near-Field Hydrophones
Authors Pete Nevill, Kevin Davies, Shanice Mohammed, Krzysztof Ubik, Richard Jupp, Ed Kragh and Philip ChristieSummaryNear Field Hydrophone (NFH) data are routinely collected during marine acquisition and historically these data were used to QC air-gun timings and/or other air-gun-related issues. More recently (last 10–15 years), these data were recorded and employed during processing to aid both 1D and 2D source-signature deconvolution. The results presented here demonstrate it is possible to obtain a high-resolution image (greater than 100 Hz) of the near subsurface (0–1 s) using passive NFH array data. Similar results are also achieved with the active NFH array although noise handling in processing is more difficult. NFH data for imaging, can be a natural by-product of conventional marine seismic acquisition, with minimal additional processing cost. Potential uses could include: replacing requirements for a high-resolution 2D acquisition for site surveys, utilizing a source-only vessel for localized overburden and reservoir 4D monitoring, as well as improved deghosting and integration with distributed acoustic sensor vertical seismic profile data.
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Broadband De-Signature for Air-Gun Arrays
Authors R. Telling and S. GrionSummaryDe-signature processing of broadband seismic data demands reliable signature estimation in the band 2–200 Hz. Here we discuss estimation of signatures via inversion of near-field hydrophone data. This uses a model for the propagation of energy from each source point to each hydrophone in the array, incorporating bubble motion and ghosting at the sea-surface. In the standard approach we solve for a set of notional sources, assuming a simple model for the ghost, with rough sea effects treated statistically using a frequency and angle varying reflection coefficient corresponding to the observed sea-state.
We discuss the successes of this standard approach and observed problems, specifically with predicted ghost amplitudes, that in practice leads us to parametrize the model using effective sea state parameters often larger than observer logs suggest. The physical reasons for this are linked to onset of cavitation in the water column. We then present results for an alternative approach, employing additional hydrophones, that solves directly for the down-going part of the signature without need for ghost model parametrization. We assess the quality of signatures estimated via this approach, their application to de-signature processing and examine sensitivity of this inversion to noise compared to the standard parametrized approach.
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New Wave Seismics
More LessSummaryThis talk will focus on the field of seismic oceanography. Seismic oceanography exploits acoustic energy reflected from temperature and salinity boundaries in the water column to map oceanic structure at unprecedented horizontal resolutions. New insights into four-dimensional ocean dynamics that marine seismic data has to date provided, along side potential future applications, will be reviewed. In addition, the challenges and opportunities presented by processing water column seismic reflection data, as opposed to sub-surface datasets, will be discussed. Advances include: estimating temporal changes in the water column during seismic data collection; quantifying noise, water turbulence and wave energy from seismic data; and inversion techniques to extract the high resolution temperature and salinity structure with precision uncertainty. Such non-conventional approaches to seismic data processing, alongside better quantifying the influence of the water column on the quality of marine seismic data, will be of interest to this community. Finally, the integration of autonomous systems into the acoustic mapping of oceanic thermohaline structure will be considered.
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Finite Difference Modelling Including Dynamic Speed of Sound in Water
Authors K. Davies, J. Stefani, L. Zhuo and T. JohnsenSummaryIdentifying and implementing fit for purpose Earth complexity in synthetic modelling is an important part of testing the ability to recover broadband signal. Effects of dynamic speed of sound in water Vw(x,y,z,t) have widely been recognized in 3D marine processing, although 4D often highlights the complexity and requirement for adequate measurement and correction. Corrections are applicable to conventional 3D and VSP marine applications, even if the effect is not immediately obvious. In 2013 Chevron in conjunction with WesternGeco investigated, quantified and ranked the effects of Vw(x,y,z,t) in streamer 4D using finite difference modelling. However, modelling with Ocean Bottom Node data presents additional challenges. In 2015, Chevron built a complex 4D dynamic water column model closely based on Vw(x,y,z,t) observation. The model was specifically designed for evaluation of 4D OBN data. Processing in conjunction with CGG demonstrated the significance of no-correction, after typical correction and with alternative methods, including Up-Down deconvolution, the latter demonstrating a remarkably good efficient solution. Furthermore, Up-Down deconvolution enables efficient source signature deconvolution for enhanced bandwidth. Including the complexity of Vw(x,y,z,t) in modelling has enabled estimation of 4D signal to noise and design of mitigation measures in both acquisition and processing.
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Why 4D Broadband is the Next Standard for Reservoir Monitoring Studies?
Authors D. Lecerf, B. Caselizt and C. ReiserSummaryFor the last ten years, the seismic industry has been offering broadband 3D seismic. Broadband acquisition and processing technologies are appealing for 4D time-lapse surveys. As for any 4D requirement, they must deliver seismic signal repeatability for an extended frequency range to be qualified as a 4D broadband solution. Multisensor streamer systems offer an optimum platform for acquiring 4D broadband data in an efficient way. Deeper tow depths give a better signal-to-noise ratio and allow an improved acquisition weather window. In addition, multi-sensor recording provides receiver ghost-free data insensitive to the sea state.
We will discuss the different options for introducing a new broadband dataset into the 4D reservoir cycles. In a genuine 4D broadband acquisition context, we will describe how the repeatability of broadband streamer data can compete with the repeatability of 4D OBN and why an extended bandwidth 4D signal allows to better characterize the seismic image variations due to reservoir production. Finally, we conclude that 4D broadband acquisition, processing and interpretation can complete the challenge of quality and efficiency improvement.
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Multi-Parameter Waveform Inversion Using Broadband Data from the North West Shelf of Western Australia
Authors C. Manuel, J. Washbourne, D. Sibley, L. Duranti, M. Merino and B. BoulahanisSummaryWe present a multi-disciplinary approach to determine overburden anisotropy on the North West Shelf of Australia. Beginning with a synthetic dataset we demonstrate the significance of incorrect overburden anisotropy in seismic imaging and develop a workflow for solving the problem. This is followed by application of our workflow to a field case study involving the use of geological inference, log-based rock property relationships, and surface seismic data. Comparisons are made between applying the workflow using conventional and broadband streamer datasets acquired over the same area.
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High-Frequency Full-Waveform Inversion: Just How High Should You Go?
Authors A. Ratcliffe, S. Bretherton, B. Xiao and R. HaackeSummaryWe pose the question, “how high a frequency should you go to in FWI?” The answer depends on your objective: the traditional processes of imaging, reservoir characterization, and interpretation, or as a potential complete replacement for these. In this paper we discuss and demonstrate the impact of the maximum frequency in the FWI velocity model on these processes, using data sets from the North and Norwegian Seas.
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Low Frequencies Unlock Visco-Acoustic Full Waveform Inversion Capability in Trinidad
Authors C. Theriot, T. Fox and S. BarronSummaryA large Ocean Bottom Node (OBN) survey acquired off the east coast of Trinidad has allowed Shell the opportunity to deploy another application of its visco-acoustic Full Waveform Inversion. The setting is ideal, with pervasive shallow gas covering the area resulting in dispersion and attenuation. A proper and accurate earth model including the Q anomalies in this area is more accurately determined with visco-acoustic FWI over tomographic methods given shallow water depths resulting in incoherent shallow images and migrated gathers. In addition to the Q-field, the inversion will also derive the velocity field including the resulting slow-downs from these gas zones and ultimately improve image quality. The higher quality low frequencies recorded by the OBN has resulted in a more accurate and geologically reasonable model where previous Full Waveform Inversions using streamer data have left inaccuracies. And from this more accurate model, we present a substantial image uplift.
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Broadband FAZ Land Data: an Opportunity for FWI
Authors O. Hermant, A. Sedova, G. Royle, M. Retailleau, J. Messud, G. Lambare, S. Al Abri and M. Al JahdhamiSummaryThere are very few applications of full waveform inversion (FWI) on land data. This is commonly attributed to data-specific challenges. However, modern broadband full-azimuth (FAZ) land surveys offer an extraordinary opportunity for applying FWI. They have dense surface and offset sampling X and Y directions, and contain very low frequencies down to 1.5 Hz. We demonstrate in this study and in other real data examples from the Sultanate of Oman that it is possible to benefit from the broadband spectrum of modern land acquisitions to obtain a high resolution velocity model reliably using FWI.
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Does Broadband Address the Cycle Skipping in Complex Areas?
By D. VighSummaryFull-waveform inversion (FWI) is a high-resolution model building technique that uses the entire seismic record content to build the earth model but have struggles with cycle skipping . Conventional FWI usually utilizes diving and refracted waves to update the low-wavenumber in other words the background components of the model; however, the update is often depth-limited due to the limited offset range acquired. To extend conventional FWI beyond the limits of the transmitted energy, we must use reflection data as well with broad band preprocessing.
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Towards Lateral Broadband
Authors R. Soubaras and B. GratacosSummaryNew acquisition techniques and the evolution of broadband processing in the past ten years have enabled the extension of the frequency bandwidth from the conventional three octaves bandwidth [10Hz–80 Hz] to a six octaves broadband bandwidth [2.5Hz–160Hz]. Despite this impressive achievement, some problems still remain:
- The broadband processing sequence has become very complex.
- This processing sequence makes a heavy use of sparse tau-p transforms in steps like receiver deghosting and regularization. However, the underlying assumption that a shot point can be locally decomposed in a few linear events can be questionable.
- The lateral resolution has not increased in the same proportion as the vertical resolution.
In order to solve these problems, we show that we can obtain a significant increase in lateral resolution by using for the final imaging a least-squares migration with ghost and multiple modeling, allowing the deghosting, regularization and multiple attenuation being handled by the inversion. This is assessed on a real 3D dataset with depth-slices showing an increase in wavenumber bandwidth similar to the increase already obtained in frequency bandwidth.
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Imaging Including Internal Multiples: Influence of Broadband Acquisition
By E. VerschuurSummaryNowadays there is a strong trend towards considering multiples as genuine part of the seismic response and, therefore, including this in the imaging process.
For surface multiples, this has already shown successful in various applications over the last decade.
For the correct imaging of internal multiples, there is a debate whether removing internal multiples can be more fruitful than trying to image them. In this paper we will show the added value of properly including internal multiples in the imaging stage, where the transmission effect is also being accounted for. In this way the imprint from a multiple-generating overburden is also minimized.
Finally, it will be demonstrated that acquiring data with a broadband acquisition set-up, the effect of internal multiples is already greatly reduced, which inceases the abilities to treat them properly in the imaging stage.
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Thermal Modelling of Magmatic Geothermal Systems: the Role of Deep-Seated Heat Sources
Authors G. Gola and A. ManzellaSummaryWe present the results achieved in the framework of different research projects, i.e. the Geothermal Atlas of Southern Italy, the Image, the Descramble and the Gemex Projects, mainly focussing on the thermal aspects of four geothermal fields developed in magmatic setting. We applied an integrated method in order to set-up numerical models able to simulate the conductive-convective thermal structure of the Ischia Island (southern Italy), Long Valley Caldera (eastern California), Acoculco caldera complex (eastern Mexico) and Larderello-Travale (central Italy) geothermal systems. We propose a numerical approach implemented in a Finite Element environment capable to evaluate the contribution of the main variables that characterize the magmatic heat source and the geothermal reservoir. The final 3D thermal models were achieved via the optimization of the available temperature measurements in deep boreholes tacking into account the thermal effects of the interplay between the free convection and the topographically driven groundwater flow, the reservoir permeability and the thermal load released by the parametrized heat source. Our results contribute to better understand the relationship of magmatism to geothermal resources in continental settings.
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Laboratory Studies of Organic and Inorganic Geothermal Tracers at Superhot and Supercritical Conditions
Authors Muller Jiri, Sissel Opsahl Viig and Helge StraySummaryLaboratory studies have been performed at testing stability of organic and inorganic tracers at super-hot and supercritical conditions. Both static and dynamic tests have been performed at specially constructed equipment which can tolerate such hard conditions. In some cases these tests indicate no rapid thermal degradation of the tested tracer candidates within the time frame of the performed stability test (2 months). In other cases the experiments indicate interactions between the rock material and the tracer candidates.
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Seismic AVO Inversion for Geothermal Reservoir Characterisation
Authors E. Dalgaard, K. Bredesen, A. Mathiesen and N. BallingSummaryA field case is demonstrated to show how 2D seismic AVO inversion together with well log analysis can aid reservoir characterization of a geothermal play in the northern Zealand of Denmark. From the seismic inversion it is possible to interpret different lithologies and estimate porosities via links established at well logs. Several connected high porosity sands were predicted, and with an expected temperature of around 50C in the target zone this gives room for a potential good geothermal reservoir. With this specific field case it is demonstrated how seismic AVO inversion can be applied where geothermal reservoir characterisation is needed in order to obtain a better understanding of potential geothermal plays.
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Geothermie 2020: Exploration and Development of Geothermal Energy in Geneva
More LessSummaryThe deployment of renewable energy sources for both power and heat production is accelerating in Switzerland, a trend that will continue, thanks to the 2050 Swiss Energy Strategy that aims at gradually phasing out nuclear power by reducing the energy consumption and increasing heat and electric power generation from renewable energy sources. Geothermal energy will be an important resource to supply heat and power for industrial, agricultural and domestic use.
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Exploration of Geothermal Reservoirs: an overview and future opportunities
By P. JoussetSummaryAn overview of geophysical exploration methods for geothermal reservoir is proposed. Focus is made on the integration of seismic attributes and resistivity structures, with examples from Iceland and Mexico. Future targets include magma chambers and urban environment.
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Learn to Invert: Surface Wave Inversion with Deep Neural Network
Authors S. Hou, S. Angio, A. Clowes, I. Mikhalev, H. Hoeber and S. HagedornSummaryWe propose a hybrid analytics and machine learning approach for large-scale surface wave inversion (SWI) for shear-wave velocities in the shallow overburden. A sparse grid of 1D velocity models are inverted using analytic optimization. Then, a deep neural network (DNN) with three hidden layers is trained using a spatially sparse subset of the data and non-linear inversion results. Finally, we use the DNN to predict the velocity model for the whole survey. This approach is demonstrated on a real high density land project. In comparison to the purely analytical approach, the hybrid analytic-ML method estimates a more reliable shear velocity model over the whole survey with significant reduction in computing time. We end with a discussion around the potential of this type of method for other geophysical inverse problems and seismic processing.
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Neural Network Travel-Time Tomography
More LessSummaryTravel-time tomography is a non-linear inverse problem. Monte Carlo methods are increasingly used to provide probabilistic solutions to tomographic problems, but these methods are computationally expensive. Neural networks can be used to solve some non-linear problems at a much lower computational cost. We show for the first time that a form of neural network called a mixture density network can perform fully non-linear, rapid and probabilistic tomographic inversion using travel-time data. We compare two methods to estimate the Bayesian posterior probability density functions: first a vector of networks are trained such that each estimates the marginal posterior probability distribution of wave speed in one grid cell; second, a single network estimates the entire posterior probability density function across all cells. While both methods provide estimates of the true structure in the means of their distributions, their uncertainty estimates differ: when separate networks are trained to solve for wave speeds at each location in the model the standard deviations exhibit uncertainty loops, as expected, whilst a network trained to solve for speeds on the whole model at once does not. The former method is therefore likely to be more robust.
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Including Physics in Deep Learning – An Example from 4D Seismic Pressure Saturation Inversion
Authors J.S. Dramsch, G. Corte, H. Amini, C. MacBeth and M. LüthjeSummaryGeoscience data often have to rely on strong priors in the face of uncertainty. Additionally, we often try to detect or model anomalous sparse data that can appear as an outlier in machine learning models. These are classic examples of imbalanced learning. Approaching these problems can benefit from including prior information from physics models or transforming data to a beneficial domain.
We show an example of including physical information in the architecture of a neural network as prior information. We go on to present noise injection at training time to successfully transfer the network from synthetic data to field data.
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Stratigraphic Segmentation Using Convolutional Neural Networks
Authors D. Civitarese, D. Szwarcman and E. Vital BrazilSummaryThe Discovery of new possibles reserves is an critical activities for the oil and gas industry. The most used methods to understand the sub-surface are based on seismic surveys. however, the process of the interpretation of these surveys are very expensive and due to the volume of data it overload the human capabilities. On the other hand, deep learning techniques have been increasingly applied in several areas of science to help in tasks that were considered human-centered, such as image classification and language translation, among others. We propose a machine learning methodology to classify seismic data at the pixel level, producing an interpretation mask suggestion. Our methodology comprises three main parts: model selection, dataset preparation, and training. We also present Danet-FCN3, a deep neural network specifically designed to classify seismic images at pixel level resolution. We have recently demonstrated that our deep learning models can distinguish among different rock layers helping the expert to interpret new seismic images. The dataset preparation processes the raw post-stacked data and the interpretation labels to produce training, validation and testing sets.
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Removing Elastic Effects in FWI Using Supervised Cycled Generative Adversarial Networks
More LessSummaryWe use a CycleGAN to map acoustic synthetic data to elastic data, and to map elastic field data to acoustic data, and use the resulting data to perform acoustic FWI on a 3D field dataset that shows strong elastic effects at top chalk. Using machine learning to change the effective physics of field data has many other potential applications.
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Extracting and Classifying Graphic Information from Geoscience Unstructured Documents Using Deep Learning Based Computer Vision Approaches.
Authors H. Blondelle, J. Micaelli and P. KaurSummaryFormation Evaluation Logs (FEL) and composites integrate together a lot of information gathered together by the well-site geologist while drilling and logging. But, since they are frequently published as unstructured documents, they are not easy to use as a source of information in digital business processes.
We had the opportunity to support our customer Equinor to “read” lithological columns, O&G show symbols, and geological descriptions from FEL and composites using a state-of-the-art computer vision approach called YOLO and our indexing solution named iQC. A process based on YOLO and iQC transforms the graphical information into usable, numeric and text values that can be consumed by business databases. Computer vision and semantic analysis models were trained on composite logs, which were tagged by subject matter experts with expected labels. The developed models automatically detect and draw bounding boxes around target objects in test documents. This paper details this experiment, lessons learnt and provides some perspective to improve the accuracy of the first results obtained.
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Stabilized Super Resolution Deep Generative Networks for Seismic Data
Authors R.S. Ferreira, E. Zabihi Naeini and E. Vital BrazilSummaryHigh-resolution seismic data enable us to characterize the reservoirs with higher accuracy and/or detect smaller targets. Enhancing the seismic bandwidth can be achieved with broadband acquisition, various processing algorithms or a combination of both. In contrast to classic spectral matching type processes, we propose to take a different approach by using deep Generative Adversarial Networks (GANs). In theory, they can reconstruct the seismic data both temporally and spatially. This is inherent by design given the convolutional architecture of the GANs. That means GANs allow recovering the frequency content or the missing traces in seismic data. We propose amplitude encoding and histogram equalization to stabilize the performance of GANs on seismic data and show promising preliminary results for typical seismic processing and interpretation applications.
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Machine Learning For DAS Microseismic Event Detection
Authors S. Horne, A. Baird, A. Stork and G. NaldrettSummaryEmerging acquisition systems based on fiber optic technologies such as Distributed Acoustic Sensing are enabling dense spatial and temporal sampling of strain fields. This has resulted in a large increase in the volume in the volume of data and the rate at which data is generated. These increases can be interpreted as satisfying two of the 3 ‘V's of ‘Big Data’ i.e. Volume and Velocity (the third V refers to data Variety). In this presentation we show how we have used big data technologies such as Apache Hadoop and Apache Spark to tackle these data issues for the specific problem of microseismic event detection. Furthermore, traditional approaches were thought to be unlikely to efficiently scale to these new data so we turned to machine learning approaches based on computer vision. Rather than trying state of the art technologies such as Convolutional Neural Nets we decided to try a mature technology used for face detection known as a Haar Cascade. We have tested this approach on field data and found that this approach can work well and are motivated to try newer machine learning techniques with the expectation of moving beyond microseismic event detection.
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EarthNET a native cloud web based solution for next generation subsurface workflows
Authors D. Oikonomou, E. Larsen, B. Alaei, G. Stefos and S. PurvesSummaryFaster and better data driven decision-making and shorter times to first oil and gas top the list of expected benefits that digital technologies can drive for upstream oil and gas companies. In the oil industry, Artificial Intelligence (AI) and Machine Learning (ML) tools have already moved from R&D projects into G&G tool boxes, slowly transforming the subsurface workflow.
We will discuss about cloud platforms and demonstrate how such an integrated platforms provides both the data access and applications required to apply ML at scale with examples that include integration of multi regional datasets.
We will show that such platforms are not only enhancing further creativity and enabling data driven decisions but in addition will shorten time to oil which seems to be the next challenge of the industry.
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Can Machines Learn to Pick Horizons in Post Stack Data?
Authors L. Yalcinoglu and C. StotterSummaryThe presented method applies a supervised deep learning (DL) method to detect the horizons throughout a seismic dataset with high detection accuracy.
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Towards Subsurface ML Metrics
More LessSummaryUse of metrics are key in application of machine learning in any domain. Good metrics allow us to assess performance of algorithms, gain insight into the behaviour of models and understand the impact of model and parameter choices as well as data and feature selections. Shared metrics allow research and engineering communities share knowledge and communicate effectively at a high level, helping progress and reproducibility.
In applying ML in the subsurface, the first port of call is to use standard ML performance metrics such as accuracy, f1_score and r2 score. These metrics are well know but generic. In some cases they provide effective performance indicators, more so in classification tasks. However they generally don't provide much insight into why model is achieving a particular level of performance, or measure performance in terms of expected or acceptable subsurface behaviour.
In this workshop session, we aim to further the discussion on why development of a common set of meaningful subsurface metrics is important for the our community. We highlight some of the gotchas and shortcomings with typical metrics used in machine learning classification and regression tasks and we propose some potentially routes forward.
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Ore content estimation based on spatial geological data through 3D convolutional neural networks
Authors B.W. W S R Carvalho, D. Civitarese, D. Szwarcman, P. Cavalin, B. Zadrozny, M. Moreno and S. MarsdenSummaryCurrent tools for identifying new exploration targets for gold are built for geologists to manually interpret data acquired from different sources. Scaling this approach to larger projects is not a trivial task. One possibility to tackle this problem is to use data-driven predictive modeling to discover relationships in the data which can then be applied throughout a mine to more readily identify exploration targets.
Here, we propose a methodology based on machine learning that takes as input data points in space describing measured geological information in a mine, correlates this with the level of gold mineralization in known places through a 3D convolutional neural network, and uses the obtained model to estimate the level of gold mineralization in every region of the mine that has available geological information.
We compare the obtained model with a baseline model and show that it outperforms the baseline in all the metrics used, providing a much more accurate estimate of presence of economic gold for geologists in their investigations.
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Surface-To-Borehole Tfem Data Acquisition System Development and Field Test
More LessSummaryA surface-to-borehole time-frequency domain EM (TFEM)data acquisition system has been developed and a successful field test was conducted in the Liaohe Oilfield of China. The field test has successfully acquired downhole 3-component magnetic field and vertical electrical field surface-to-borehole EM data the first time using a 3 km long surface dipole current source at two source locations. The first long dipole current source was placed in the redial direction of the borehole and 3 km away from the wellhead, and the second dipole current source was placed in the redial direction of the borehole and 3.5 km away from the wellhead. The dipole current source injected square wave current into the subsurface to generate the underground TFEM signal. A newly developed 4-level borehole TFEM receiver array was used to record surface-to-borehole TFEM data from the depth of 1,000 m to 1,800 m in the borehole. Each level of borehole TFEM receiver contains a 3-component time domain induction coil package installed in the center of receiver tool and two electrodes are located at each end of the receiver tool with a spacing of 10 m. The surface-to-borehole TFEM data show characteristics of subsurface formation electromagnetic properties and changes at different depth.
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Surface-to-Borehole CSEM for Waterflood Monitoring: Modeling & Data Analysis
Authors D. Colombo and G. McNeiceSummaryA full-scale 3D surface-to-borehole CSEM survey was carried out to map the position of the current waterfront around a test well in a producing oil field. Pre-survey modeling studies and experimental results show agreement in terms of measured signal levels, repeatability errors and expected sensitivity to targets. The vertical electric field shows the largest sensitivity to the spatial resistivity distributions in the reservoir. The obtained results provide the baseline for future time-lapse surveys targeting the monitoring of the water-oil saturation changes.
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Surface-to-Borehole CSEM for Waterflood Monitoring: 3D Inversion Strategies
Authors G. McNeice and D. ColomboSummaryA 3D surface-to-borehole CSEM survey was acquired in a research well located in a producing oil field to monitor the movement of the waterfront. Variations in the data acquired in the initial baseline survey showed excellent spatial correlation with resistivity variations predicted through reservoir simulation. A 3D finite difference model of the anisotropic overburden and wellbore casing is used to invert for the resistivity of the reservoir surrounding the well. Comparison to borehole observations suggest the CSEM survey robustly recovers the reservoir resistivity distribution within 1.5 km of the well.
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Borehole-to-Surface Time-Lapse CSEM Measurements Across a Producing Oil-Field: Repeatability, Vertical Electric Fields, 3D Inversion Including Steel-Casings
More LessSummaryControlled Source Electromagnetics (CSEM) measurements were acquired across an onshore oilfield in Northern Germany between 2014 and 2018 comprising up to 5 transmitters, 29 surface and 3 borehole receivers.
Repeatability of data is an essential prerequisite for reservoir monitoring. Our results suggest that repeatability of CSEM measurements depends on source-receiver distances, source-polarisation, and relocation errors, in particular at sites close to the source. Best repeatability was observed for receiver stations at 2–4 km distance from the source and frequencies <20 Hz. At these stations, phases and amplitudes usually agreed within ±1° and ±5% between repeat measurements.
The vertical electric field (Ez) was measured with a newly developed receiver chain, suspended in a 200 m deep observation borehole. Although amplitudes of Ez are about one to two orders of magnitude smaller than amplitudes of horizontal electric fields, Ez data are stable and show excellent repeatability within <±2° and <±5 % during the 3 years.
For 3D inversion of the field data set, we developed a new methodology which accounts for first order effects of steel-cased wells in the oil field. We demonstrate that both energised and passive well casings can strongly influence the outcome of 3D inversion.
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Challenges in the Analysis of Surface-Borehole EM Measurements. Reviewing the Field Distortion due to Metallic Casing
Authors N. Cuevas, D. Colombo, G. McNeice and M. PezzoliSummaryIn this paper a discussion is presented of the main aspects describing the challenges expected and encountered in recording and analyzing STB/BTS data in the presence of the steel casing. To this end, numerical simulations of the casing effect are analysed in relation to the many unknown of the system, i.e. casing, overburden and reservoir properties. The discussion highlights the evident need to either remove the distorting component of the field due to the current flow in the casing, or to directly model the response of the entire system, i.e. including the localized highly conductive anomaly of the metallic casing. In the former case, an estimate of the casing effect could be obtained from the recorded data, but the accuracy may not be enough to remove the casing response while keeping a x100 weaker response of the reservoir. In the later case, aside from the numerical burden of the modelling exercise, the properties of the casing may not be known accurately enough to discriminate the subsurface response from the total field dominated by the casing effect.
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Joint Inversion: One Mans Perspective
More LessSummaryTremendous advances have been made in the last two decades in joint inversion of multiple data sets. Coupling of all forms of geophysical data and flow data have been demonstrated. Advances in methods to link the data sets and associated parameters, both by structural and rock-physics coupling approaches have been key to the impressive results currently being demonstrated.
Looking forward it seems that applications of joint inversion to improve our reservoir flow models holds the largest benefit compared to other applications such as structural imaging. In the reservoir application, combining of both structural and rock-physics coupling in joint inversion of seismic, EM, gravity and flow data will undoubtedly be needed to maximize results.
Advances in theory and compute power will lead to stochastic MCMC based sampling techniques as the preferred method for joint inversion. MCMC techniques ability to provide a global solution, the easy of incorporating multiple and desperate a priori information, and accurate uncertainty estimation will all drive the field toward stochastic approaches.
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Joint Assimilation of Electromagnetic and Seismic Data - a Stochastic Approach
By F. VossepoelSummaryThe complementary nature of seismic and electromagnetic (EM) data asks for joint inversion of these data sets for reservoir characterisation and monitoring. EM data contain valuable information on the reservoir lithologies and have the ability to discriminate between hydrocarbon- and brine-filled rock. As the EM signal is diffusive, the resolution of the data is generally low, and is best combined with seismic data and appropriate prior models that help constrain the solution space.
To account for uncertainties in the data in a statistically robust manner, we propose to make use of data assimilation techniques. This approach is especially attractive in monitoring applications where dynamic models provide a physically consistent prior estimate of the reservoir characteristics and its state evolution. After providing an overview of the possibilities for joint assimilation of EM and seismic data, a number of data-assimilation examples will illustrate the advantages and disadvantages of the various approaches.
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Deep Learning Applications in EM Imaging
Authors J. Byun, S. Oh, K. Noh, D. Yoon and S. J. SeolSummaryDeep learning is now one of the most powerful techniques for solving various scientific and engineering problems. These deep learning techniques have recently begun to be applied in the field of subsurface imaging. As a part of the effort, we have applied the deep learning techniques to the imaging of subsurface from electromagnetic (EM) data. This presentation introduces three cases of the application: salt delineation and monitoring of injected CO2 using towed streamer EM data sets and kimberlite exploration using airborne EM data set. The results with significant qualities open up the possibility of the deep learning as an alternative of the conventional inversion techniques.
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Machine Learning for Geoscience Applications
By A. AbubakarSummaryWith roots in Artificial Intelligence (AI), machine learning has evolved over several decades with contributions from various scientific disciplines. In the nineties, remarkable techniques such as probabilistic graphical models, kernel, boosting, and random forest methods emerged and since the mid-2000s, with availability of large datasets and improvements in computational power led to advances in neural network based methods with various deep learning architectures. The latter has resulted in some remarkable innovations in the recent years, and led to wide and visible successes for a spectrum of scientific and commercial applications. With these modern methods, it is now feasible to solve problems with significant underlying complexity; and that too with remarkable accuracy and flexibility. Oil and gas industry acquires large and complex datasets for exploration and field development purposes. However, these datasets are not being optimally used to extract useful information. We believe that with the recent advances in machine learning and computational power, advanced machine learning methods can be used to not only extract useful information from these complex datasets but also reduce the man power costs to process and makes sense of these datasets. Recognizing this potential, over the past several years, we have been actively researching and developing numerous modern machine-learning applications in various domains, including the geosciences. Through examples, our focus will be on the potential of machine learning to address complex geoscientific problems such as well log processing, interpretation, correlation and seismic interpretation.
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EM for Land-Based Reservoir Monitoring: Achievements, Challenges and Road Ahead
Authors D. Colombo, G. McNeice, R. Adams and M. DeffenbaughSummaryWe analyze the development, current challenges and the future opportunities of land-based electromagnetic (EM) techniques for reservoir monitoring. Specific reference is made to technologies connecting the surface and the borehole as the most promising approaches to 3D reservoir characterization and monitoring. A few prominent and recent experiments are providing a common base for the definition of an emerging technology for oil reservoirs as well as for CO2 storage monitoring. The path to the establishment of an effective oil field technology shall pass through engineering and standardization for which the current technical challenges need to be identified. The road ahead includes especially the solution of steel casing and pipe responses, new sensors for permanent reservoir monitoring, fiber optics, high voltage signal transmitters, multi-physics integration approaches, integration with reservoir simulators, use of novel machine learning techniques and the adoption of a multi-physics culture enabling the integration of multiple observations to better describe the reservoir dynamics.
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Benefits of Joint Seismic and EM Inversion for Hydrocarbon Development Projects
By P. VeekenSummaryReservoir monitoring is illustrated on a Borehole CSEM case study. Ways are examined how to increase the detection power. The following parameters proved important: grid step of computation scheme, incorporation of induction effects, 3D survey design, constrained inversion, guided inversion, timelapse approach. Joint inversion of independent multi-physics datasets is useful: seismics, MT, CSEM, gravmag. It reduces the solution space and enhances the discrimination power for thin beds. Each dataset requires their own quality control. A sequential or simultaneous approach can be chosen. Prestack inversion and AVO effects give access to rock physical parameters. Analysis of the generated data is a challenge. A multi-disciplinary integrated approach is stimulated by the methodology. Joint inversion makes economic sense due to better reservoir management decisions. Improvements and innovations in the workflow should be pursued: better computers, efficient algorithms, extraction of relevant attributes, mixed attributes, principal component analysis, machine learning, neural network analysis.
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Ten Years of Becoming Less Uncertain About the Uncertainty of Our Uncertainty Estimates…
By C. HoeltingSummaryWe present a partial review of industry's efforts over the past decade or so to produce physically-based estimates of velocity and depth uncertainty, focusing on the trade-off between velocity and anisotropy. We will discuss the necessity and the difficulty of applying constraints (beyond those inherent in surface seismic data) during this estimation process. We have chosen material and examples with the intent to help initiate a lively discussion.
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Resolution Constraints in Bayesian McMC Travel-Time Tomography
More LessSummaryThe resolution matrix from a linearized inversion scheme is integrated into a probabilistic Markov chain Monte Carlo algorithm to provide multi-variate compensations to the random pertubartions. This comes at no additional computational cost other then the prior computation of a generalized inverse, and it improves acceptance ratio, mean step length and mixing properties. The efficiency is tested with a synthetic example from refraction seismic travel-time tomography.
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Near-Real Time 3D Seismic Velocity and Uncertainty Models from Ambient Noise, Gradiometry and Neural Network Inversion
Authors A. Curtis, R. Cao, S. Earp, X. Zhang, S. De Ridder and E. GalettiSummaryProducing seismic wave speed models of the Earth's interior with full uncertainty estimates is a grand challenge of geophysics. It is relatively easy to produce uncertainty estimates by linearising (approximating) the nonlinear physics relating models to data, but in strongly nonlinear problems such estimates can be almost worthless. Nonlinear solutions are usually calculated using Monte Carlo methods, requiring weeks of computation due to the high dimensionality of parameter spaces. In addition, using seismic interferometry to obtain reliable surface wave dispersion data from ambient noise often requires several days of recordings.
Clearly both recording and computation timescales must be reduced dramatically to allow ambient noise tomography in near-real time. Recording times must be reduced by changing methods used to obtain dispersion curves. Computation time is constrained by two mathematical results: the ‘curse of dimensionality’ precludes exhaustive Monte Carlo search in high-dimensional parameter spaces, and “No-Free-Lunch” theorems state that improvements over exhaustive search require substantial additional a priori information. Nevertheless, we show that recording times can be reduced to the order of minutes, and that common a priori physical assumptions plus a separation of up-front and real-time computation allow 3D velocity models and uncertainties to be obtained in less than an hour.
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Evolutionary Algorithms: from optimization to uncertainties?
More LessSummaryThe main goal of this study is to assess the potential of Evolutionary Algorithms to solve highly non-linear and multi-modal tomography problems (such as traveltime tomography) and their ability to estimate reliable uncertainties. Classical tomography methods apply derivative-based optimization algorithms that require the user to determine the value of several parameters (such as regularization level and initial model) prior to the inversion as they strongly affect the final inverted model. In addition, derivative-based methods only perform a local search dependent on the chosen starting model. Global optimization methods based on Markov Chain Monte Carlo that thoroughly sample the model parameter space are theoretically insensitive to the initial model but turn out to be computationally expensive. Evolutionary algorithms are population-based global optimization methods and are thus intrinsically parallel, allowing these algorithms to fully handle available computer resources. We apply three evolutionary algorithms to solve a refraction traveltime tomography problem, namely the Differential Evolution, the Competitive Particle Swarm Optimization and the Covariance Matrix Adaptation - Evolution Strategy. We apply these methodologies on a smoothed version of the Marmousi velocity model and compare their performances in terms of optimization and estimates of uncertainty.
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Surrogate-Based Forward Uncertainty Propagation for Large-Scale Seismic Wave Propagation
Authors P. Sochala, F. De Martin and O. Le MaîtreSummaryThe goal of uncertainty quantification in a forward problem is to estimate the uncertainties in the model output induced by uncertainties in model inputs.
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Use of Tomography Velocity Uncertainty in GRV Calculation
SummaryRecently, new seismic products for quantifying uncertainties associated with delivered seismic products have emerged. It is therefore important for reservoir risk analysis to pass the uncertainties along with the data delivered. In an exploration context, identifying potential traps by combining structural and sedimentology information is a challenging process due to the lack of well data to validate the potential presence of reservoir in the lead. In this paper, we illustrate the integration of tomography velocity uncertainties in a resource evaluation workflow and demonstrate the impact on gross rock volume (GRV) distributions for the ranking of potential prospects.
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Combining Ensemble Transform Kalman Filter and FWI for Assessing Uncertainties
Authors J. Thurin, R. Brossier and L. MétivierSummaryFull Waveform Inversion (FWI) is an iterative inversion method whose purpose is to retrieve high-resolution models of subsurface physical parameters. Because FWI relies on the solution of a non-linear ill-posed inverse problem, uncertainty estimation is a crucial issue in practical applications, both in seismology and exploration seismic. While uncertainty assessment is a strongly desired feature for FWI, it remains a challenging problem. In this presentation, we investigate uncertainty estimation within the framework provided by ensemble data-assimilation strategies. We combine the Ensemble Transform Kalman Filter and FWI. We review the concepts underlying our ETKF-FWI method, discuss its limitations and appeals for uncertainty estimation, and illustrate it on a 2D multiparameter inversion of an exploration scale field dataset.
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Transdimensional Bayesian Thermochemical Joint Inversion of Seismic, Gravity and Surface Elevation Data
Authors D. Molodtsov and J. FulleaSummaryWe introduce a transdimensional probabilistic inversion algorithm in which seismic traveltime, gravity and surface elevation data are inverted for thermochemical parameters of the crystalline crust.
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An Overview of the Geophysical Challenges in Monitoring and Locating Induced Earthquakes with Downhole Networks
Authors S. Minisini, C. Willacy, E. Van Dedem, J. Li and J.W. BloklandSummaryWe analyze the challenges that may be encountered when trying to locate microseismic events recorded by downhole networks in complex geological settings. The observed seismic events are characterized by significant complexity due to the high velocity contrasts and full waveform modeling is used to better understand and interpret the arrivals. Conventional location methods based on ray tracing for microseismic event location and moment tensor inversion may not be optimal for such an environment. To address this challenge, we developed a full waveform matching methodology for locating microseismic events and show its application on real dataset.
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Avoiding Pitfalls and Extracting Value: Lessons for Induced Seismicity Monitoring.
Authors B. Dando, V. Oye, B. Goertz-Allmann and A. WuestefeldSummaryWe present case studies from microseismic monitoring including the Groningen gas field and the Decatur CCS site. We highlight often overlooked monitoring considerations including choice of location algorithm and waveform complexity, and demonstrate how modelling can avoid or mitigate the negative effects. Interpretation of microseismicity can lead to valuable insight into reservoir processes. However, to extract the most value, analyses must go beyond standard processing techniques. We show how combining waveform cross-correlation, full-waveform modelling, travel-time and ray-path analysis, and relative relocation methods can provide the necessary constraints to deliver improved interpretations from microseismic data sets.
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The Need for Advanced Traffic Light Systems for Risk Mitigation of EGS Projects
Authors P. Meier, F. Bethmann and O. ZinggSummaryMany enhanced geothermal systems (EGS) projects rely solely on traditional magnitude based traffic light systems in order to mitigate seismic risks. However, in several geothermal projects (e.g. Basel and St. Gallen in Switzerland, Pohang in South Korea) traditional traffic light systems have fallen short to limit induced seismicity to a predefined level of event magnitude.
Our risks studies for the planned EGS project in Haute-Sorne in Canton Jura (Switzerland) and the evaluation of the Basel case highlight the need (1) for an advanced traffic light system taking into account the spatial distribution of seismicity and integrating continuously throughout all project stages new data and especially information about fault structures and (2) to update the risk studies throughout the duration of the project.
Such a procedure has been the basis for the permit by the authorities of the Canton Jura for the planned EGS project in Haute-Sorne and is also recommended by the “Good practice guide for managing Induced Seismicity in Deep Geothermal Energy Projects in Switzerland” published by the Swiss Seismological Survey.
Firstly we list some of the conditions of the permit and secondly we discuss selected points in light of the experiences of the Basel and Pohang EGS projects.
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Using Vertical DAS Arrays for Continuous Monitoring of Induced Seismicity
Authors A. Lellouch, Z. Spica, B. Biondi and W. EllsworthSummaryOne of the most promising applications of DAS to the field of seismology is the possibility to permanently record data with downhole arrays with a much denser spatial sampling than would be possible with conventional geophones arrays. The data recorded by these arrays can be used for continuous (or periodical) monitoring of both natural and anthropogenic seismicity. Using two datasets we show how the data recorded from vertical arrays can be used: 1) to estimate 1D velocity models and 2) to detect events with high-sensitivity based on the events moveout computed with the estimated velocity models. The continuous nature of the DAS array allows us to accurately retrieve a high-resolution P-velocity model and extract an S-velocity model that couldn't be estimated from conventional VSP data.For one of the datasets we compare our detection results with an existing events catalogue. We find we are able to detect above 75% of cataloged events within a radius of 15 km and one weak new (uncatalogued) event.
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The Role of Parallel Fracture Networks for Induced Seismicity in the Duvernay Formation
Authors Nadine Igonin, James P. Verdon, M. Kendall and David EatonSummaryFluid injection and hydraulic fracturing can cause induced seismicity. Two commonly proposed causative mechanisms are pore pressure and stress perturbations, or a combination of the two. Since most studies of induced seismicity due to hydraulic fracturing to date have been recorded on a regional scale, they lack the resolution to shed light on some of the key questions currently in the field. Using a high-quality dataset obtained during hydraulic fracturing in the Fox Creek, Alberta area, the detailed mechanisms of fault activation were examined. This experiment, dubbed the Tony Creek dual Microseismic Experiment (ToC2ME), contains several events over MW 2.0, and shows high-resolution fault activation. Seismic anisotropy derived from induced events was used to determine the direction of a pre-existing fracture network, which is attributed to be the main conduit by which fault activation was triggered. Pore pressure modelling was carried out to show that the delay times between injection and activation are consistent with the time necessary for a sufficiently significant pore pressure perturbation to reach the main fault features.
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Modelling the Seismic Response of Flooded Faults in an Ultra-Deep South African Gold Mine
Authors J. Gerber and G. Van AswegenSummaryIn this study, the displacement-discontinuity boundary-element method is used to simulate large-magnitude, slip-type failures associated major geological structures in an ultra-deep, South African gold mine. The above-mentioned slip-type failures are simulated using a so-called RIDE algorithm, and the modelled seismic response is described in terms of the expected number of seismic events with some potentially-damaging size. In this case, the modelling results are well correlated with the observed seismic response.
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Induced Seismic Background Disturbance Due to Geomagnetic Pulses
By S. RiabovaSummaryThe purpose of this work is to determine the response of the microseismic background to pulsed geomagnetic variations for the conditions of the mid-latitude Mikhnevo Geophysical Observatory of Institute of Geosphere Dynamics of Russian Academy of Sciences. During pulsed variations of the magnetic field of SSC and SI types, in most cases, increased variations of the seismic background are found. Spectral analysis shows that the induced variations in the seismic background are mainly observed in the frequency range of 0.01 – 0.1 Hz (in some cases, 0.001 – 0.1 Hz). The statistically significant correlation between amplitudes of induced microseismic background and geomagnetic pulses is indicated by high correlation coefficients calculated by different methods and by surrogate data analysis. The quantitative relationship between the studied variables was obtained. The application of methods of statistical data processing made it possible to establish that it is the geomagnetic field variations that entail a change in the microseismic background and with some delay.
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The Future of 4D Subsurface Modelling: Reflections from a Multi-Sector Conference
Authors G. Burridge, T. Finkbeiner, J. Herwanger, W. Hohl, R. Plumb, K. Royse, J. Booth and G. McKinleySummaryIn February this year, the Geological Society of London held a conference entitled 4D Subsurface Modelling: Predicting The Future. Its objective was a novel one: To explore the lessons to be gained by comparing the four industrial geology sectors: Oil & Gas, Mining, Civil Engineering and Geothermal.
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On Strategies for Consistent Local Geomodel Updating
By G. CaumonSummaryModel updating is an important way forward to efficiently incorporate new information about the subsurface. Four local components are needed to address common needs: finding the subregion to be updated, updating the discrete model features (topology) locally or semi-globally, locally updating the feature geometry and locally updating the associated properties. We present a few existing strategies and highlight that topological and geometric editing are essential for geologically consistent and automatic or semi-automatic geomodel updates.
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Big Loop Reservoir Forecasting in the Machine Learning era
Authors F.O. Alpak and M. Araya-PoloSummaryWith the arrival of Machine Learning (ML) techniques as effective alternatives to many legacy modeling steps, classical static and dynamic reservoir modeling workflows need re-adjustment. In particular, we will focus on Big Loop (BL) approaches to reservoir modeling, where subsurface disciplines create an integrated representation of the subsurface, calibrated to static and dynamic information, for reliable field development and reservoir management decision making. The commonality is
Finally, we show some of the specific ingredients of an evergreen ML-driven Big Loop workflow.
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Technology of Modeling and Simulation Integration and its Application
Authors Zhang Yuxiao, Xia Jian and Han ZhiyingSummaryDue to oil development areas are gradually entering high or extra-high water cut stage and the distribution of remaining oil is extremely sophisticated, a set of integrated technology for fine reservoir modeling and reservoir numerical simulation is formed to perform a deep research, according to different characteristics of reservoir and difficulties of exploration and development, The technology is based on the multi-scale data matching of the fine reservoir modeling, combined with the method of numerical simulation. The technology follow the rules of “analyzing contradictions, resolving contradictions” to continuously optimize the reservoir geological model and fluid distribution model by using the method of well-to-seismic integration. The series of integrated technology involve in many technical fields and a variety of reservoir types. It has been successfully applied in several development areas.
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A Multi Domain Machine Learning Approach for Reliable Production Forecast
Authors S. Walia and V. AgarwalSummaryBig Loop™ solution meets these evolving needs and provides oil and gas operators with a platform to manage the challenges and complexities of modern reservoirs. The Big Loop workflow results in a calibrated flow model that is consistent with the underlying geology. The workflow is easy to update and allows experts to spend more time analyzing the results and building a common understanding of the reservoir instead of manual adjustments. Big Loop's automated, ensemble based, stochastic workflow tightly integrates the static and dynamic domains, ensuring that all the relevant reservoir uncertainties are captured and used as input parameters integrated to the reservoir simulator. By adjusting uncertainty ranges these input parameters, multiple realizations of the static and dynamic model, constrained by factors such as production history, are created via an iterative loop. (Big Loop - Trade mark of Emerson)
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