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82nd EAGE Annual Conference & Exhibition
- Conference date: October 18-21, 2021
- Location: Amsterdam, The Netherlands
- Published: 18 October 2021
1101 - 1137 of 1137 results
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Active and Passive MASW Analysis of DAS Data from an Active Landslide
Authors S. Cole, P. Clarkson, M. Karrenbach, V. Yartsev, B. Dashwood and D. GunnSummaryA DAS fiber-optic array is used to monitor an active landslide. MASW analysis of the shallow velocity structure is performed using both active-source data and thirty hours of passive seismic recording. Inverted velocities from the two datasets show consistency and have features that correlate with the location of an active landslide scarp.
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Unlocking new exploration potential in the Gulf of Suez through the application of modern OBN seismic
Authors G. Byerley, A. Rehan, K. Mondy and A.I. AbouelelaSummaryIn February 2020, Neptune Energy signed an operated exploration license with the Egyptian General Petroleum Corporation for Egypt’s North West El Amal (NWEA) Offshore Concession located in the southern part of the Gulf of Suez. The committed work program included new seismic acquisition to solve the complex imaging problems associated with the pre-salt structures defining the majority of remaining potential resource in the area. A model-driven approach was used to evaluate the various challenges and to simulate possible acquisition geometries to define the best solution for imaging prospective rotated fault blocks beneath the salt. The modelling outcome was an optimized design of a new ocean-bottom node (OBN) seismic acquisition program. This paper highlights the processed results to date and how the new OBN data is being used to de-risk new exploration opportunities in such a mature and prolific basin.
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PP-PS joint inversion and horizon mapping analysis of the 3D3C multicomponent dataset of Bandurria Norte, Argentina
More LessSummaryThe focus of this study was to perform an enhanced characterization of the unconventional play of the Vaca Muerta formation. In 2017 a multicomponent test survey was acquired over an area of about 13km² with the aim of reliable fraction and rock elasticity information to reduce uncertainty of this shale oil development project. To test this concept, we employed a joint inversion method developed by Hampson et al. (2005) and Russell et al. (2005) that makes use of the compressional wave (PP) and the shear wave (PS) components. PP/PS Post-Stack joint inversion delivered straightforward and fast results for P- and S- impedance and gave enhanced insights into the elastic properties of the formation. Comparing horizons picked in PP and PS time provided fast insight and helped in identifying a subtle fault that can have a significant impact on later development stages. On the Vp/Vs ratio dataset, a result from the joint inversion, low values in upper part of lower Vaca Muerta formation were observed. These values could reflect zones of overpressure which can be caused by leak-off of pressure through the underlying Tordillo formation. Another possible explanation might be that this low Vp/Vs values reflect zones of higher organic content.
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Simultaneous velocity and reflectivity inversion: FWI + LSRTM
Authors Y. Yang, J. Ramos-Martinez, D. Whitmore, G. Huang and N. CheminguiSummaryWe present an iterative non-linear inversion method to simultaneously estimate both velocity and reflectivity. The core of the inversion workflow is a full acoustic wavefield modeling relation parameterized in terms of velocity and vector reflectivity. A key aspect is the separation of the low- and high-wavenumber components of the gradient based on inverse scattering theory, enabling the sensitivity kernels to update the velocity and the vector reflectivity, respectively. The estimation problem is formulated as a multi-parameter adjoint-state inversion where the trade-off between velocity and reflectivity is minimized through scale separation. Our approach is equivalent to performing Full Waveform Inversion (FWI) and Least-Squares Reverse Time Migration (LSRTM) in a single framework using the full wavefield. The output of the inversion is a detailed velocity model together with an accurate estimate of the earth reflectivity with compensation for incomplete acquisition, poor illumination, and multiple crosstalk. The new approach reduces the turnaround time of imaging projects by combining velocity model building (FWI) and imaging (LSRTM) into a single inversion process with minimal data pre-processing.
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Selective Waterflood System Paving the Way for Effective Waterflood and Reservoir Management
By H. TyagiSummarySelective waterflood injection system has proved to be an effective tool to increase the sweep efficiency and reduce the residual oil saturation from wells completed in a complex and heterogenous reservoirs in mature oil field. This is a cost-effective technology which has played a significant role to unravel the untapped reserves from existing wells.
Enhancing well performance and recovering hydrocarbon resources are vital, often neglected tasks to improve field output, but this approach is limited by constraints from other wells and facilities. An integrated approach to closely monitor the entire production system—wells, reservoirs and surface networks over time is the most efficient way to enhance overall field value.
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An Integrated Formation Evaluation Approach Evaluated the Basement Temperature Anomaly
Authors C. Ciuperca, C. Badulescu, M. Erlström, E. Mats and A. HammarSummaryA holistic formation evaluation approach to characterizing the naturally open fractures zones explained the basement negative temperature anomaly occurrence.
For this purpose, using measurements of high resolution dual slim imager, acoustic cross-dipole, density, photoelectric factor, spectral gamma ray and temperature, few formation evaluation techniques were applied, such as structural facies identification, fracture aperture calculation, sonic anisotropy, brittleness index magnitude and polarity, Stoneley fracture identification, which, coupled with mudlogging data offered a conprehensive understanding of the naturally occurring fractured zones over the thermal conductivity anomaly.
The presence of fractured facies identified on borehole images, decrease of density values, the occurrence of sonic anisotropy, changes in the brittleness index polarity, increase of the fracture density, increase of the fracture aperture and the presence of Stoneley reflection chevrons were used as arguments of water influx through conjugated open natural fracture system which generated the negative thermal anomaly.
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Comparison of permeability for different fracture models in laboratory experiments on hydraulic fracturing
Authors E. Novikova and M. TrimonovaSummaryIn the course of the presented study, 6 laboratory experiments on mini hydraulic fracturing were conducted using special laboratory setup with triaxial loading of the model sample. The pressure-time dependencies recorded during the experiments were analysed using the Nolte method. The main goal of this study was to estimate the pseudo-linear flow regime and to calculate permeabilities for different models of fractures. Additional aim of the work was to compare the obtained values with the real value of permeability, that is known for the material used for modeling the medium where the hydraulic fracture is formed and propagated.
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The impact of fluid yield stress on hydraulic fracture propagation
Authors E. Kanin, E. Dontsov, D. Garagash and A. OsiptsovSummaryWe investigate the influence of fluid yield stress on propagation of a radial hydraulic fracture in a permeable reservoir. The hydraulic fracturing fluid rheology is governed by Herschel-Bulkley model including yield stress and non-linearity of the shear stress. The rock is linear elastic, and the fracture is formed due to fluid injection at a constant volumetric rate. The crack propagation criterion follows the theory of linear elastic fracture mechanics, and Carter’s leak-off law describes the fluid leak-off into formation. We developed two numerical approaches to compute the problem solution: fully numerical (Gauss-Chebyshev quadrature and Barycentric Lagrange interpolation techniques) and approximate (the global fluid balance equation combined with fracture tip asymptote). The presented simulations representing typical field cases demonstrate that the yield stress can lead to a fracture with a shorter radius and larger aperture compared to the radial fracture model with simpler power-law fluid. We derived limiting propagation regimes characterised by dominance of certain physical phenomena and built parametric maps showing their applicability domains. Such analysis enables one to identify whether the yield stress provides a substantial impact for any given problem parameters.
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Pore-Scale Study of the Residual Trapping of Air in a Doddington Sandstone Using In-Situ Micro-CT Imaging
Authors P. Bakhshi, S. Ghanaatian, O. Shahrokhi, S. Garcia and M.M. Maroto-ValerSummaryDeep saline aquifers have been identified as promising sites for storing large volumes of CO₂. As the plume of the injected CO₂ progresses through the formation, the residual trapping mechanism activates and entraps the CO₂ due to the natural or engineered flow of water. Core-scale findings and pore-network flow models’ estimations for residual trapping in saline aquifers range from 10% to 90% of the total injected volume of CO₂. This widely varying range of CO₂ trapping potential necessitates the pore-scale observation of this phenomenon to facilitate a fundamental understanding of the controlling parameters of this trapping mechanism.
To investigate this phenomenon at the pore scale, we have designed and developed a unique micro-CT core flooding system, which is an excellent tool for providing valuable 3D information of flow processes at realistic subsurface conditions. The main components of this in-situ imaging flow rig are an X-ray transparent flow cell, capable of withstanding elevated pressure and temperature to provide the conditions of typical deep saline aquifers and a high-resolution CT scanning device. By using this rig, we study the process of residual trapping in an air/brine system within a Doddington sandstone at the atmospheric pressure and ambient temperature conditions.
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DAS System Modeling and Data Validation for CCS Monitoring
Authors A. Chavarria, C. Laing and S. ColeSummaryMonitoring of CCS facilities with fiber optic has increased rapidly over the last six years. As part of carbon capture and storage monitoring fiber optic tools have been deployed at different test sites. This monitoring has taken advantage of enhancements in distributed acoustic sensing (DAS) technology. Here we present a workflow to assess optimal optical acquisition to acquire seismic signals, passive and active, that are needed for permanent reservoir surveillance. We present data from new DAS interrogators units capable of sensing long distances that would encompass CCS projects being developed on land and offshore. Data presented here includes active seismic within a shallow CCS facility connected to a long fiber optic line. In addition the long range technology is validated for passive induced seismicity surveillance. We demonstrate that the fiber optic DAS systems have high sensitivity needed for high resolution imaging of storage facilities and for detectability of potential induced events during permanent injection monitoring.
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Reservoir characterization using petrophysical analysis of the Volve Field, Norway
Authors O. Oyetunji and R. StewartSummaryBefore any seismic inversion work is carried out, a conventional project starts with a rock physics feasibility study during which we evaluate whether facies classification is feasible.
In this study, an attempt was made at identifying the reservoirs present at well locations in the Volve field located in the Norwegian North Sea. Rock physics modeling and AVO analysis were applied in an integrated approach to study the seismic response of the reservoirs and assess the feasibility of distinguishing different lithologies and fluid types.
Perturbational Modeling (porosity, lithology, and fluid) was also carried out on the Hugin sands. The result shows the effect of changing porosity, volume clay, and fluid on the elastic properties. Due to the good porosities and thickness of the sands, good Lithofacies and fluid discrimination were observed especially in the upscaled and AVA domain.
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Velocity and Q estimation from the separated upgoing and downgoing wavefields in VSP data
More LessSummaryQuality factor (Q-factor) evaluates the attenuation of seismic wave propagation, playing a fundamental role of reservoir characterization, which can be obtained accurately from Vertical Seismic Profile (VSP). The common methods usually use the downgoing wavefields in VSP data. However, the downgoing wavefields consist of more than 90% energy of the spectrum of the VSP data due to the energy fraction of the upgoing and downgoing wavefields, which makes difficult to estimate the viscoacoustic parameters accurately. Thus, a joint viscoacoustic waveform inversion of velocity and Q-factor is proposed to measure the difference between the separated upgoing and downgoing wavefields in VSP data based on the multi-objective functions. A simple separating step is accomplished by the reflectivity method to obtain the pure individual wavefields in VSP data, and then a joint inversion step is carried out to make full use of the information of the individual wavefields and improve the convergence of viscoacoustic waveform inversion. The sensitivity analysis about the velocity and Q-factor shows that the upgoing and downgoing wavefields contribute differently to the viscoacoustic parameters. Numerical examples and a field test indicate the accuracy and efficiency of the proposed method.
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A time consistent waveform inversion (TWIN) method
Authors R. Valensi and R. BainaSummaryBorn-modelling based Reflection Waveform Inversion (RWI) approaches explicitly separate the model space into smooth and a high-frequency components (background and reflectivity models, respectively). As these two models are strongly coupled, sequential inversion approaches are sub-optimal and reflectivity-velocity consistent schemes have been proposed.
We propose a reflectivity-velocity consistent RWI approach, handling the reflectivity-velocity coupling issue by enforcing the data invariance at zero-offset during the inversion process.
As a result, the corresponding misfit function gradient contains an additional term (compared to conventional RWI) due to the reflectivity-velocity coupling effects.
We compared on the Chevron benchmark model, the conventional RWI (sequential inversion of reflectivity-background models) and the proposed approach. We show that the TWIN approach provides a velocity model with significant improvements compared to sequential (conventional) RWI approach, especially in preventing strong vertically-extended artifacts in deep parts of the model.
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Building low frequency model with Deep Learning for seismic inversion in complex geology without structural model
Authors T.M.S. Tengku Hassan, C.S. Lee, R. Bekti and J. TingSummaryThe conventional low frequency model (LFM) have limitations: uncertainty of spatial variability away from the wells, the uncertainty of the structural model and stratigraphic architecture. It is also challenging to build complex geology structural model. We propose using Deep Feed-forward Neural Network (DFNN) with attributes from seismic partial stacks and seismic velocity to create LFM of elastic properties for Constrained Sparse Spike Inversion. The methodology incorporates training of well curves, additional information from seismic partial stacks and trend from seismic velocity and wells. It has shorter turnaround by not having to include structural model, and is suitable for complex geological settings.
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Evaluation the Impact of Mineralogical Composition of Reservoir Rocks on Wettabiliity during Surfactant Flooding Processes
Authors H. Esfandiari, S.R. Shadizadeh and R. AbdollahiSummaryAchieving the newest technique for EOR process in existing reservoirs is one of significant matter in future petroleum industry. Among of all influential parameters, wettability might be the most challengeable factor to produce oil in all stages of oil recovery. In wettability evaluation many factors such as chemical structure, surface properties of the rock, composition of the oil and etc have to be taken into account. The surface properties of minerals play an important role in elucidating the behavior of reservoir minerals in presence externally added reagents like surfactants.
This paper presents experimental investigation mixed adsorption of nonionic (Triton X-100) and anionic (SDS) surfactants on minerals of reservoir. This alteration was measured based on the contact angle method. The results obtained show the nonionic Triton X-100 surfactant is better than anionic SDS surfactant for wettability alteration of quartz surface; however, for calcite and dolomite surfaces wettability alteration by SDS is more effective than Triton X- 100. Mixture of surfactants is more effective than either of them alone to alter the wettability of all surface pellets.
Keywords: Wettability alteration; Contact angle; Triton X-100; SDS; Calcite; Dolomite; Quartz.
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Partitioned-Wavefield Adaptive Subtraction
Authors A. Ali, J. Wu, C. Lapilli, Z. Wu and V. GovindanSummarySeismic, multiple attenuation, surface multiples, least square, curvelet, FK-decomposition, meta-parameter, deep marine, multiples,
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The Utility of HLD/NAC to Guide Surfactant Selection and Design
Authors A. AlKhateeb and A. AlSofiSummaryA surfactant/oil/water microemulsion is a complex system. The phase behavior of such systems is extremely complex to accurately capture and properly describe. The Hydrophilic-Lipophilic Difference and Net-Average Curvature (HLD/NAC) model has been suggested to offer the necessary understanding for the design of surfactant formulations and injection slugs. In general, for any model to be of value in design and optimization, it needs to offer a reasonable degree of uniqueness. Therefore, in this work, we use an in-house HLD/NAC model to investigate the utility and power of HLD/NAC EOS to guide the screening and design of surfactants for EOR applications. We investigate the utility of the model from a uniqueness standpoint. In the in-house simulator, three surfactant parameters (surfactant head-area, length and molecular weight) are used to model the phase behavior as a function of brine salinity. Those parameters are generated by fitting the HLD/NAC solubilization predictions to inputted observations. With that single and multi-variate sensitivity analyses were performed. The sensitivity results suggest the non-uniqueness of the three surfactant characteristics. Accordingly, and at least in its current form of implementation, the HLD/NAC model doesn’t seem capable of guiding the selection, design and/or optimization of surfactant formulations for EOR applications.
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Early stage noise removel using a convolutional autoencoder
Authors J. Walda and D. GajewskiSummaryNoise is a major concern in seismic data and influences the processing and interpretability of seismic data at various steps. However, noise has a certain pattern, which can be exploited by machine learning algorithms, that rose drastically in popularity within the last decade. We aim to remove random noise at an early stage in the processing workflow in the shot-gather domain. We use an unsupervised approach without the time consuming necessity of generating labels.
In our work, we use an autoencoder, that resembles the U-Net structure but uses a ResNext50 encoder variant. The autoencoder aims to reconstruct its input. Due to the design of an autoencoder, such reconstruction is never perfect and omits the least correlating contributions, which is usually noise. We apply our approach to a marine dataset from the Levantine Basin in the Mediterranean Sea. We are able to remove without damaging primary signal, apart from the sea floor reflection, which acts as an outlier during training.
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Adaptive reservoir model based on POD-Galerkin ROM
Authors D. Voloskov and D. PissarenkoSummaryIn this work, the adaptive approach for reservoir simulation, based on POD-Galerkin ROM, is discussed. The proposed technique is based on the idea of utilizing the information contained in the POD basis constructed for specific model configuration to build a basis for a new model setup. The adaptation is performed with the use of a small set of snapshots from the updated model configuration. The required number of snapshots is significantly smaller than one required for constructing a new POD basis from scratch. The proposed approach allows us to reduce the computational resources needed for the offline stage of POD-Galerkin ROM, thus enabling the use of the POD-Galerkin ROM for a variety of production optimization and history matching problems.
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Physics-guided deep learning using Fourier neural operators for solving the acoustic VTI wave equation
Authors T. Konuk and J. ShraggeSummaryMany real-world seismic modeling and imaging applications require computing frequency-domain numerical solutions of acoustic wave equation (AWE). However, obtaining such solutions in media characterized by strong parameter contrasts and anisotropy poses significant practical challenges to existing numerical solvers, especially for 3D scenarios. Physics-informed neural networks (PINN) provide a computationally efficient alternative approach for AWE solutions. However, PINNs solve only a single instance of AWE and need to be re-trained for each different subsurface models and frequencies. Fourier neural operators, on the other hand, can solve AWE for a wide range of models and frequencies with a single set of network configuration and parameters. This method, though, requires a tremendous amount of data, which can be difficult and expensive to obtain. Here, we propose a methodology that combines PINNs with Fourier neural operators to learn AWE solution operators that are valid for a wide range of frequencies without requiring any training data. We present two numerical examples that demonstrate the capabilities of the proposed method in modeling the acoustic wavefield accurately and efficiently in the frequency domain.
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Correction of Terminal Saturations for Capillary End Effects
More LessSummaryOil displacement experiments constitute a major component of special core analyses for both improved and enhanced oil recovery (IOR and EOR) operations. Capillary end effects (CEE) is a well-recognized phenomena that affects the accuracy of unsteady-state (USS) oil-displacement results. Huang and Honarpour presented the procedure of correcting for CEE in 1998. They derived the equations and demonstrated the procedure for correcting water terminal saturation in drainage of water-wet media. However, since their pioneering work, their methodology has not been extended to correction of oil terminal saturation for imbibition in oil-wet media—despite the well-recognized and impact of CEE on oil-displacement studies. Therefore, in this work, we revisit Huang and Honarpour (HH) pioneering method and extend it to the correction of CEE of terminal saturations for imbibition in oil-wet media. The extended method was also applied and validated using special core analysis data for a Middle Eastern carbonate. In the process, we present the base and extended methods in a concise and clear fashion that is of utility to the practicing reservoir engineer and petrophysicist.
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Deep learning-based dealiasing for estimated surface-related multiples from limited sources
Authors D. Zhang and E. VerschuurSummaryThe main prediction engine in surface-related multiple elimination (SRME) is the multidimensional convolution process, where data sampling plays an essential role for accurate surface multiple prediction. Therefore, fully sampled sources and receivers are preferred. If especially the source sampling is far from ideal, the estimated multiples will suffer from the severe aliasing effect. Consequently, this can lead to poorly estimated primaries. Interpolation of coarsely sampled sources is not a trivial task and computation intensive. Dealiasing on the estimated multiples from limited sources might provide a potential solution. In theory, this dealiasing problem is highly non-linear, which suits well for deep learning (DL)-based methods. Therefore, we propose a U-Net-based approach to dealiase the estimated surface multiples from limited sources. Applications on two subsets of the field data demonstrate the effective performance of the proposed method.
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Multi-method pore space analysis of the Portland sandstone
Authors B. Mehalli and K. TitovSummaryIn this study, we investigate the pore space of a sample of the Portland sandstone using various methods with different resolution, and based on different physical principles (Mercury injection capillary pressure, MICP, Micro-computed tomography, µ-CT, and Spectral induced polarization, SIP). According to their physical principles, each method provides either the pore body size (µ-CT) or the pore throat size (MICP and SIP). Moreover MICP characterizes the pore-throat radii, whereas SIP rather gives the pore-throat lengths. The objective of this work was to compare results provided by each technique in a common parameter framework.
The comparison approach between the different techniques is based on the relationships of ‘the incremental porosity – pore size’. Our results show that the recovered porosity can increase when using a method with higher resolution. We believe each method gives a specific attribute of the pore space topology similar to reflector attributes obtained in exploration seismology. We also believe that extensive works must be done to improve our understanding of these attributes to better characterize the pores space topology, and, consequently to better predict the transport and storage properties of soils, rocks and sediments.
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Best Practices-design for Scale Reduction During Produced Water Reinjection (PWRI)
Authors S.A. Mostafavi, S. Riahi, M. Mavaddat and A. BigdeliSummaryWater disposal treatment is one of the most important environmental challenges in oil industry. Produced water is hazardous because of its impurities, such as different type of salts, natural inorganic and organic materials, injected chemical and high salinity concentration. In spite of injectivity risks, environmental concerns force industry from surface discharge of produced water to produced water reinjection (PWRI), which is known as the method for produced water treatment. During PWRI, the most common problem is scaling which is highly affected by thermodynamic condition and water chemical composition. Thereafter, it is critical to identify influence of different variables and potential problems related to scales. In this study the OLI ScaleChem software was used to investigate the scaling problem. Results reveal that the common scales are strontium sulfide and calcite carbonate, and If EOR scenario is considered, one-order diluted refinery water is helpful while refinery water is a better candidate as IOR injection fluid.
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Evolution of the Orange Basin; Cretaceous Deepwater Fold-and-Thrust Belts to Cenozoic Mass Transport Systems
More LessSummaryThe focus of this study is on the deepwater Orange Basin, offshore SW Africa, in which several DWFTB systems are found. Previous studies have mainly focused on the 2D seismic interpretation of the Orange Basin, which is naturally limited. In this study, the availability of high-resolution, 3D seismic reflection data will allow us to constrain the strato-structural architecture of the deep-water Orange Basin from a Cretaceous DWFTB system to the overlying Cenozoic deposits using Schlumberger’s Petrel E & P software package for seismic interpretation. Understanding the architectural elements of southern Africa’s passive margin, and the tectonic evolution of the DWFTB systems contained within, is important in building on the scientific knowledge known of what occurs in these settings worldwide and in further constraining prospective sites for petroleum exploration in similar settings.
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Natural fractures prediction in a lacustrine carbonate reservoir integrating 3D structural restoration and seismic AVAz techniques
Authors P.H. Silvany, M.N.C. Araujo, R.B. Bunevich, J. Almeida, C.E.B.D.S. Abreu and C.E.L. PereiraSummaryLow permeability carbonate reservoirs constitutes significant reserves of oil and gas for Petrobras’ E&P sector. Micro-porosities above ~ 10% in these sedimentary sequences allow the accumulation of significant volumes of hydrocarbon. However, the predominance of pore throats smaller than 10 microns produces low permeability in this type of rock, making it difficult for the flow of interstitial fluid during production. This constitutes the main challenge for making production feasible. The shortage of static and dynamic data in most reservoirs of low permeability makes it extremely difficult to understand the spatial distribution of the different scales of heterogeneities and, consequently, influence in obtaining realistic flow scenarios. In this work, an integrated supervised methodology is proposed for the characterization of natural fractures in a low permeability lacustrine carbonate reservoir in Santos basin, Southeastern Brazilian margin. To achieve the objectives of the study, a workflow was developed that involves the actions, briefly described below: (i) descriptive and kinematic analysis at multiple scales of the brittle structures; (ii) understanding of lithological control in the deformation process; (iii) quantification of the deformation in different phases of movement by 2D / 3D structural restoration techniques and (iv) analysis of seismic anisotropy.
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Remote sensing, geological and geophysical integration – Study case in Valinho de Fátima, Portugal
More LessSummaryThe work intends to establish a methodology for the characterization, evaluation of the potential and choice of sites in rock masses, for the implantation of ornamental rock explorations.
A first phase was carried out with lithological and structural assessment at a regional scale, identifying in the Geological Map of Portugal (1: 50k). The next phase consisted of thematic geological mapping, with lithological and structural assessment at the local level.
In parallel, geophysical exploration was carried out using the Electromagnetic Method in the Time Domain [TDEM], to map the variation and distribution of resistivity, underground, and its correlation with the existing and modern cartography.
This document presents the results of remote sensing, using Copernicus Sentinel-2 composite images, in which the use of automatic assisted classification allows a quick thematic mapping of the surface, created from field knowledge and bibliographic knowledge of the region, for evaluation radiometric signatures for the objects of interest in the designated area.
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Water Saturation Prediction in the Reservoir Zone of a Gas Field using SVR Method
Authors S.A. Afzali Fatatouei and M. BagheriSummaryEstimation of reservoir parameters is one of the most important factors in oil and gas reservoir investigations. One of the most important parameters for modeling reservoir is water saturation, which any errors in evaluating it can cause sever financial damages. By core analysis, calculating water saturation is possible, but there isn’t core in every place. Therefor there is a need to estimate water saturation. In this investigation for estimating water saturation, support vector regression was used, which is one of the applications of machine learning and can solve the curse of dimensionality. The well’s data was used for training in the method and water saturation was considered as labelled data. In the end for obtaining the best estimation two different kernels was used.
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Quantitatively Evaluating the Preservation of Deep-water Channel Architecture using 3D Synthetic Seismic from Outcrop
Authors T. Langenkamp, L. Stright, S. Hubbard and B. RomansSummaryForward seismic reflectivity models can be used to interpret depositional architecture and stratal surfaces. However, such studies often stop short at a qualitative assessment of the link between underlying depositional architecture and seismic resolvability, lacking a quantitative assessment. This study addresses this gap with a direct quantitative comparison of 3-dimensional facies architecture predicted from seismic with a “ground truth” to quantify heterogeneity facies associations and architecture preserved in inverted seismic data. The primary goal is to quantify how facies architecture information is preserved in and predicted from inverted seismic reflectivity data. The objective is to explore what the variables are that impact correct vs incorrect facies classification. With increasing seismic frequency, channel axis becomes harder to predict while mass transport deposits became easier to predict. Facies in shallow reservoirs are easier to predict than in deep reservoirs. Disorganized channel systems show greater facies predictability than organized systems due to greater AI contrasts. This study highlights what architectural information is preserved in 3-dimensional inverted seismic data, built from outcrop data of a deep-water system, which can aid directly in interpretation, reservoir prediction, and modelling.
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Entropy-driven particle swarm optimization for reservoir modelling under geological uncertainty – application to a fractured reservoir
Authors B. Steffens, V. Demyanov, D. Arnold and H. LewisSummaryIn this work we introduce a novel reservoir modelling workflow where modelling is assisted by an entropy-driven particle swarm optimizer. Producing a representative range of reservoir models that cover geological uncertainties in an effective way is a challenging task. We therefore make use of entropy to ensure that the ensemble of generated models adequately reflects the available information and provides diversity that reflects the associated variability in fluid flow behavior. The workflow is tested on a synthetic case study of a fractured reservoir.
The results indicate that the entropy-driven PSO is able to prevent the diversity of the ensemble of models from collapsing whilst staying within the bounds of a predefined expected dynamic flow response. It is also shown that the entropy-driven PSO outperforms a standard PSO in this task. Secondary outcomes from the workflow, such as a spatial entropy map, provide a great tool for further uncertainty assessment and can be used to identify swept or unswept reservoir regions and the regions where more information is needed to reduce the uncertainty.
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Segmentation of Digital Rock Images Guided by Edge Feature Using Deep Learning
More LessSummarySegmentation of digital rock images is a crucial and basic step in digital rock process, and equivalent elastic parameter and fluid properties calculated from the digital rock can be affected by the result of segmentation. Conventional segmentation algorithm based on thresholding algorithm cannot perform a satisfying result in small structure due to noise impact. To address issues, a modified guided by prior information, edge feature, is proposed to improve accuracy of small structure. Edge feature reflects information of the effect of transport, weathered, and eroded in the deposition process, but the shape of noise and artifacts can’t reflect these information, rather show regularity due to the influence of instruments, hence boundary feature can improve the discrimination of noise. Furthermore, conventional SegNet was used to compare with modified SegNet, the former obtains 90.21% accuracy using 38-layers network, proposed approach using prior information achieves 93.07% accuracy using 30-layers network, which demonstrates less computational time and better anti-noise property. In addition, connectivity was used to evaluate segmentation result, modified SegNet shows a better similarity with origin image.
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Characterizing Subsurface Damage Zones From 3D Seismic Data Using Artificial Neural Network Approach
More LessSummaryTo further improve the quality and efficiency of subsurface fault zone image and study its geometry. Herein we adopted post-stack seismic data conditioning and a combination of seismic multi-attribute for producing a new hybrid attribute through a supervised multilayer perceptron (MLP) neural network in the Jurassic formation of Cai36 3D prospect located in the eastern part of the Junggar Basin. We first conditioned original seismic data by using the dip-steering cube extracted from the original seismic data. Secondly, we extracted conventional seismic attributes from the conditioned data sensitive to fault zone signatures. Thirdly, we selected a set of “picks” at a time slice representing the presence or absence of fault zones. Then we adopted the supervised MLP neural network to train over the selected seismic attributes extracted at the fault zone and non-fault zone positions. We obtained a new fault probability cube as new attributes. Finally, we analyzed a typical strike-slip fault zone using the new attributes. This study provides an effective way of fault zone imaging from seismic data and adds new insights into its geometry. Therefore, the workflows used here could be widely applied to other 3D surveys.
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Projection-based autoregressive neural network for model-reduced adjoint-based variational data assimilation
More LessSummaryThe adjoint method has been used very often for computation of analytical gradient, however, the generic implementation of the adjoint model often needs both significant programming efforts and computational cost. We proposed a novel projection-based autoregressive neural network (aNN) where the model-reduced adjoint is efficiently produced with the help of an easy-to-use auto-differentiation tool in deep-learning frameworks. This study restricts focus to propel orthogonal decomposition (POD) due to their physical interpretation and high scalability. Analogy to reduced-order tangent linear model, a projection-based aNN (POD-aNN) structure is proposed to accelerate the construction of adjoint model based on the reduced subspace. The POD-aNN consists of a dimensionality reduction and an intermediate non-linear transition unit which are used to produce the low-representation of the state system and approximate the time-varying dynamic, respectively. Thus we can derive a model-reduced adjoint model very efficiently. We demonstrate the performance of proposed methodology with many representative data assimilation experiments on a synthetic 2D subsurface flow model characterized by random spatially dependent parameters. The results have shown that this proposed POD-aNN obtains satisfactory results with significantly reduced computational cost and therefore demonstrates promising applicability to practical cases.
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Higher-order mass-lumped wave propagators on variable resolution, triangular meshes
Authors K.J. Roberts, A.F.G. Olender, R.D.S. Gioria and B.S. CarmoSummaryUsing the finite element method leads to a sparse system of equation that are relatively computationally expensive to solve for as compared to finite difference methods. However, by using higher-order mass-lumped triangular finite elements that lead to diagonal mass matrices, the computational cost is dramatically reduced enabling the use of unstructured triangular meshes. However, to efficiently use these elements, a cost-effective distribution of degrees-of-freedom needs to be chosen. This abstract shows results using different spatial polynomial orders of mass-lumped triangular elements while varying mesh sizes for a 2D homogeneous case and applies those results in a heterogeneous 2D case.
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Rejuvenation of the T40-T45 paleogeography model in the Flett Sub-Basin (West of Shetlands)
Authors R. Julien, H. Olsen, C. Climent and H. CromieSummaryA new paleogeographic model for the Late Paleocene to Early Eocene T40-T45 sequences ( Ebdon et al., 1995 chronostratigraphic nomenclature) for the Flett Sub-Basin (West of Shetlands Basin) has been constructed. Seismic interpretation (on 2D and 3D data) was integrated with wells information to constrain the spatial extension of the different gross depositional environments. During this period, the Flett Sub-Basin was semi-confined between the West of Shetland platform and the massive Faroe basalt plateau. A continental clastic wedge was deposited in the south west of the sub-basin whereas in the north east (at Bunnehaven and Tobbermory well location), starved deepwater settings prevailed. The basin was also marked by a multi-scale interaction of sedimentation with volcanism linked with the opening of the North Atlantic Ocean and the activity of the proto Icelandic mantle plume. Different scenario regarding sand paths (from the coastal domain to the deepwater basin) have been investigated. Despite all the efforts, no scenario prevailed on the others and Bunnehaven sands’ provenance remains ambiguous.
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Early tectonic structures concealed by the latest deformation phase might reveal exploration opportunities. An outcrop example.
Authors D. Casabianca, A. Barrier, A. Ricciato, R. Di Cuia and S. BorelloSummaryThe geological evolution of most sedimentary basins currently explored for hydrocarbon resources, involved more than one tectonic deformation phase. The earlier structures, concealed by the easily recognized latest deformation, often remain overlooked by interpreters. This might result in poor exploration decisions such as unnecessary exploratory wellbores or missed opportunities. The Maiella mountain outcrop, in the Southern Apennines of Italy, is an example where a Pliocene to Pleistocene aged compression anticline has folded carbonate sequences containing Cretaceous aged extension faults. The study of this outcrop provides insights for the deliberate search of exploration opportunities hidden within early structures in basins where multiple deformation phases are recorded.
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Lithology segmentation using deep neural network
More LessSummaryThis paper avoids the difficulties in using conventional methods in lithology segmentation task by putting the tasks in the frame of computer vision.
First, we setup a lithology dataset which contains paired topology, satellite and lithology images; Second, two heated neural networks HyperNet and UNet are introduced and applied in lithology segmentation task.
The experiments show that both HyperNet and UNet are efficient and promising for the application in lithology segmentation.
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Neural networks can increase the predicted accuracy three times than random guess, that greatly reduce the workload of professional lithology geologist.
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