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Fifth EAGE Borehole Geology Workshop
- Conference date: November 21-23, 2023
- Location: Al Khobar, Al Khobar, Saudi Arabia
- Published: 21 November 2023
20 results
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Enhanced LWD Fluid Sampling Mobility via PAD Orientation Optimization Based On Azimuthal LWD Measurements
Authors S. Otaibi, M. Abdulmohsin, M. Gouda and H. KamalSummaryNewly developed oriented LWD FTS is tested and implemented in operations to effectively sample formation fluid in challenging conditions such as unstable borehole or formations with complex reservoir heterogeneity. A spot with a sampling fluid mobility was excessively higher than that of a spot selected randomly by the conventional blind orientation; significantly reduced the total pump-out time for sampling which is especially important for unstable borehole conditions.
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Compressional Sonic Log Prediction Experiment Using Machine Learning: Algorithms and Feature Selection
Authors R. Afifi and F. AnifowoseSummarySonic logs are important parameters of subsurface rock properties, and are used in various stages of oil and gas exploration as well as field development. However, these measurements are sometimes missing in certain depth intervals due to tool failure. In this study, we are comparing the effects of machine learning methods and feature selection on the predictive accuracy of compressional sonic log (DTC). We utilized the data of five wells and studied the comparative performance of Artificial Neural Networks, Regression Trees, Support Vector Machines, and Random Forest on DTC prediction. Random forest had the highest correlation coefficient and lowest mean absolute percent error, and was thus used to test the effect of feature selection on prediction accuracy. We used different input features in three scenarios: the first used only wireline data, the second used only drilling data, and the third combined both. We concluded that wireline data is sufficient to predict DTC with high accuracy. Using drilling data alone would be useful if information on rock strength were needed in real-time, but should not be relied upon for accurate prediction. Combining both and increasing the number of features did not improve prediction accuracy.
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Reservoir Fracture Detection by LWD and Wireline Electrical Imagers
Authors M. Al-Fahmi, I.B.G. Hermawan Manuaba, W. Wang and M.R. Mat YaacobSummaryThis paper includes an evaluation of fracture detection between logging while drilling (LWD) and post-drilling wireline images. The imaging tools were electrical “micro resistivity” imagers that were used to image a sub-horizontal borehole drilled with water-based mud in a fractured carbonate reservoir. The obtained images were interpreted side by side and compared for fractures and other structural discontinuities. We observed that the wireline tool detects a few fractures and misses a great number of fractures detected by the LWD tool, which is of a slightly lower resolution. We recognize that this reflects the dynamics of reservoir-borehole fluids. In particular, two distinctive borehole conditions can explain the detection of reservoir open fractures. Firstly, overbalanced drilling forces water-based mud into fracture apertures to be detected as strong conductive features against less conductive or rather resistive lithofacies. Secondly, thick mud cake masks rock features as the tool sensors make only shallow resistivity measurements. The two conditions are in favor of using LWD imaging tools to detect, and therefore describe, reservoir fractures. This is despite the fact that LWD imaging tools are yet of a lower resolution and can encounter more drilling challenges than wireline imaging tools.
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Automated Quality Control of Cutting Photos Using Deep Learning
Authors D. Alferidah and M. MezghaniSummaryQuality checking and correcting is a crucial task for the data in the oil and gas industry. Conventionally, cutting images quality checking task is manual/visual where the human subjectivity might greatly impact the final result causing high uncertainties/risk on operational decision making. Quality checking manually all cuttings is out of reach. Developing an automated solution is the only conceivable solution.
Quality checking and filtering out bad quality cutting images, has a significant role in assisting geologists toward having more accurate and trustworthy geological analysis of the available data which leads to improving decision-making. To achieve our goal of having a smart quality control system, we developed a neural network-based transfer learning system to provide support to geologists on recognizing and filtering out bad quality cutting images and concentrating on the ones with good quality. Moreover, the developed solution controls the follow of the bad quality images by sorting the cutting images along with the quality issue of them.
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Wellbore Failure Modes and Their Time Dependency in Deformed Lista Fm Shales, Grane Field
Authors F. Bender, E. Kårstad, K. Røsvik Jensen and T. RaumSummaryEvaluation of different failure modes and their time dependency based on repeat log acoustic borehole image data and geomechanical modeling for the deformed Lista formation at the Grane oil field in Norway.
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LWD Imaging Technologies and Applications
More LessSummaryLWD imaging technologies are used in a variety of application including well placement, reservoir characterization and wellbore stability evaluation. Different technologies can be suitable for different environments. This abstract describes the different LWD imaging technologies, their capabilities and applications.
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Advanced Applications of LWD Electromagnetic Resistivity For Geo-Steering and Geo-Mapping in Complex Geology Environments
Authors A. Elkhamry, M. Fouda, A. Taher and E. BikchandaevSummaryAdvancements in ultra-deep resistivity technology enabled the utilization of 3D inversion in real time. This in turn has enabled a wide variety of applications including azimuthal well placement, thin bed mapping, lithology identification at distance and 3D fault mapping. This abstract presents a summary of these advanced applications and real examples if their utilization.
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Geological Characterisation of the Aurora CO2 Storage Site Using Borehole Image Logs
Authors A. Uhrin, A. Petrik, C. Vahle and R. MeneguoloSummaryHigh-resolution borehole image logs prove to be an essential input during the subsurface characterisation for the Norwegian Longship CO2 project, particularly when integrated with core CT-scans, photographs and descriptions, as well as biostratigraphy. Hence, like for oil and gas exploration, borehole image logs are essential for an understanding of the subsurface for CCS and geothermal projects as well. For the Aurora geological storage complex of Northern Lights, the shallow structural dip with only minor faulting/fracturing was confirmed during borehole image analysis. The depositional model of the Early Jurassic Dunlin Gp’s Johansen and Cook Fms was refined in a so far under-explored area near the Troll Field. It is suggested that the shallow to marginal marine sand units are likely connected laterally and suitable for CO2 migration. Furthermore, the study improved storage resource assessment and understanding of the CO2 plume behaviour.
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Geological Structure Identification in Geothermal Fields, Southwestern Saudi Arabia
Authors I.S. Abu-Mahfouz, J. Rafiq and D. ArrofiSummaryGeothermal energy can be extracted and transformed into other forms of energy, acting as an important source of clean and renewable energy for several countries worldwide. This energy has a huge potential to be considered a promising, sustainable energy resource in Saudi Arabia that will contribute to the country’s ambitious vision of achieving net-zero emission goals. To achieve this, an in-depth understanding of the structural and geological architecture that controls the fluid circulation within potential geothermal sites is crucial. The exceptional situation of Saudi Arabia on tectonic plate boundaries along with favourable geological conditions, such as recent active tectonism and volcanism associated with the Red Sea rifting makes the country a host of many geothermal resources. Fracture density, orientation and connectivity are key determinants of geothermal reservoir permeability and fluid flow. Hence, a good understanding of fracture networks and fault zones is essential for any adequate, site-specific field development. This study aims to identify the main geological structures/structural lineaments in two geothermal fields (located in Al-Lith and Jizan) in western Saudi Arabia and analyze their densities and delineate areas of high permeability for further geothermal field development.
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Constraining Digital Core Modelling with Grain Size Data: Methodology and Tool
Authors M. Cozzi, A. Luciani, G. Maletti, R. Wen, Y. Shu and X. LuoSummarySmall scale geological heterogeneity (below the vertical resolution of conventional logs) often receives less attention in reservoir modelling practice compared to larger scale ones. Three-dimensional digital core models represent suitable tools in this sense, addressing the transition from sub-millimetric to decimetre scales, and provide effective properties values for input to large-scale reservoir simulations. A commercial software designed to build such models was reviewed and improved to quantitatively integrate grain size distributions data from laboratory analyses for increasing consistency and robustness of digital core models. Lithological realizations are simulated as first, which rely on grain size data and bedding templates. Secondly, property realizations (e.g., porosity and permeability) are simulated and calibrated against laboratory analysis on core plug data. Calibration consists in an iterative process which minimizes the difference between real and synthetic core plug properties. New steps of the workflow are presented and described. Examples of real case applications of digital core models are reported.
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Predicting Petrophysical Properties and Sedimentary Structures From Core Images
Authors M. Cozzi, A. Luciani, G. Maletti, D. Carniani, R. Wen, Y. Shu and X. LuoSummaryDeep Learning approaches are now widely and successfully applied in building data-driven models, for instance in relation to image analysis of thin sections, subsurface core images and seismic facies. The present work focuses on subsurface core images, which are arguably still underutilized by geoscientists, and targets the predictions of petrophysical properties (e.g. permeability, first methodology) and sedimentary structures (e.g. lamination, second methodology), which outcome respectively from laboratory analyses on core plugs and from core geological description. The Transfer Learning methodology applied to the VGG19 Convolutional Neural Network represents the basis of the two methodologies. The input data consist in slabbed core photos under white light conditions. Ultraviolet core photos, core-facies and core-gamma-ray can be added to petrophysical properties prediction to improve result’s quality. The output of the methodologies is a high-resolution curve of the predicted reservoir properties at the support volume of core plugs. As far as the first methodology is concerned, the resulting curve represents the optimal input for the upscaling process at any desired support volume (e.g., logs, grid-cell, seismic). The output of the second methodology can provide a further input to the first one or can feed the process of digital core modelling.
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Reservoir Characterization and Optimization: Multi-Well Approach Using High-Definition 3D Reservoir Mapping Technology
Authors G. Santoso, T. Al-Kandari and J. DolanSummaryThe method presented in this paper uses a 3D geomodelling process with the input of novel LWD 3D Ultra Deep Azimuthal Resistivity measurements to access 3D geological features within the 120ft radius around the wellbore. The 3D volume was used to correlate the two horizontal wells drilled side by side. A certain filtering process, 2D Resistivity transverse and 1D longitudinal inversions, were used to validate micro-geological features, such as pinch out, channel, and facies change between two horizontal wells.
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Multi-Physics Ultra-High Resolution Imaging Determines the Secondary Porosity Contribution in Carbonate Reservoirs
Authors M. Abdulmohsin, R.Abdelkader Mohamed, M. Al Mubarak and J. DolanSummaryCarbonate rocks are complex in nature. The presence of heterogeneity in carbonates creates a challenge for the characterization of such rocks. Vugs are cavities in the rock created by dissolution or erosion and vuggy porosity affects the effective permeability in the reservoir. The identification of vuggy zones in the reservoir is important for the reservoir characterization. Cores and image logs are the main tools to identify and characterize the vugs in the reservoir. In this paper, two logging-while-drilling (LWD) technologies were run in the same reservoir to identify and characterize the vuggy porosity. Two different techniques were used to understand the vuggy system. The first LWD imager is a laterolog resistivity tool for water-based mud environments while the second was a dual physics imager (electromagnetic resistivity and ultrasonic) for oil-based mud systems. Both technologies provide ultra-high resolution and provide valuable information about the vugs in the reservoir. The vug density analysis is derived from advanced processing and interpretation of these high-resolution image logs. The results improved the understanding and prediction of the reservoir quality in carbonates.
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Secondary Porosity Quantification in Carbonates Based on Resistivity Borehole Image Logs
Authors M. Belayneh Woldeamanuel, M. Aljasem, P. Tarabbia and T. IlijasSummaryDissolution of carbonates to create vuggy zones commonly found in carbonate rocks has been widely covered in the literature. Storage capacity and permeability of carbonates are functions of various processes including depositional environment, texture, diagenesis, burial and uplift, tectonic history, and the chronology of events. Porosity types and distribution are products and interactions of these processes. With this in mind, this study was undertaken on carbonate fields using static and dynamic resistivity borehole image logs, calipers, the presence of total or partial losses and other dynamic indicators. We applied point counting techniques for quantifying ramiform voids and cavities from image logs on a subset of data intersecting over 3,000 feet of these features. The objectives of this study are to investigate and quantify diagenetic dissolution features identified on image logs and to assess the impact on the development of non-matrix porosity. The normal procedure for quantifying reservoir porosity is by using core plugs and logs obtained across subsurface reservoirs or analogue outcrops. We note that these methods are applicable to pore types occurring in the sub-millimeter to a few centimeters range. Plug samples cannot be acquired for features bigger than a few centimeters and significant underestimation of any connected solution channels, large vugs and cavities in carbonates can occur. Even if plugs are selected, they should be devoid of any visible vugs and fractures and this creates a bias towards the lower end of the porosity spectrum. Using a borehole image log point counting technique on a very large database, we show how we can enhance porosity deduced from the subsurface in horizontal wells for the inclusion of these dissolution features in the 3D geological model.
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Carbonates Pore System Characterization Using Multi-Scale Digital Rock Analysis
By A. AlroudhanSummaryDetailed understanding of the pore scale rock properties at micron resolution is essential for proper formation evaluation, which includes borehole log interpretation. Micro computed tomography (mCT) imaging becomes the standard tool to generate the framework, on which petrophysical models are applied. The advantage of petrophysical simulation is that the results are obtained with quick turnaround and at a lower cost compared to traditional experimental laboratory work.
This study presents a new multiscale approach to tackle the characterization of heterogeneous multimodal carbonate rocks using digital rock analysis based on mCT scanning. Multimodal carbonates are difficult to investigate because of a wide range of pore sizes, which runs the gamut from vugs (mm scale) down to micro-pores (sub-micron scale). We acquired mCT scans employing variable volumes of rock and at multiple resolutions for several carbonate samples. This resulted in a coarse- and a fine-scale 3D representations of the pores for each sample, on which the petrophysical modelling was conducted. The modelling results, particularly, the pore throat size distribution is a special feature that is used for petrophysical rock typing. Covering all pore sizes in a single mCT scan is not possible for the majority of carbonate formations.
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The Analysis of Temperature Gradients in a Mixed Carbonates Reservoir
Authors C. Ciuperca, Y. Karakeçe and S. ÇavuşSummaryBesides the classical uses of temperature log, the in-depth study of temperature gradients can provide valuable information for reservoir characterization when integrated with other logs.
In a study with mixed carbonate reservoir, the analysis of temperature vs depth cross-plot identified 4 distinct zones. Zone1 (limestone) with a negative temperature gradient of −0.0425 ⊏C/m, Zone2 shows a dramatic decrease of temperature due to the intrusion of drilling fluid thru the open natural fracture apertures during a total loss event, Zone3 (dolomitic) with a trend of +0.67 ⏋C/m and in Zone4 the gradient wasn’t reliable due to non-stabilized temperature.
Integrating a complete borehole image interpretation has revealed that even if the fracture aperture was slightly higher in Zone1 compared to Zone3, the secondary porosity and fracture density were larger in Zone3. Image structural facies analysis further revealed that Zone3 was almost entirely highly fractured with fracture type being conductive, while in Zone2 besides the highly fractured facies it occurred also other less fractured facies, with fractures being mixed resistive/conductive and conductive.
The main sources of temperature gradient variations were deemed being the host rock thermal conductivity differences and the occurrence of zones with different fracture density/porosity producing rock cooling effects.
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Multiscale Fracture Characterization by Log Data Integration and Statistical Extraction of Tectonic Features
Authors R. Berto, F. Chinellato, F. La Valle and L. SpaggiariSummaryThe characterization of subsurface fractured reservoir is a challenging task, requiring a multiscale approach. The micro-fractures (from cm to meter length) are usually studied, for characteristics and distribution, from oriented cores and well logs, whereas faults (macro-fractures, more than hundred meters length) are interpreted and mapped from seismic data.
The study of meso-fractures (tens to hundred meters length), also defined sub-seismic fractures, is still arduous to be performed with direct tools. Recent technological innovations, mainly in Wireline (WLL) and Logging While Drilling (LWD) areas, allow the mapping of features tens of meters far from the wellbore. However, it is not a conventional approach, that requires a dedicated planning and a significant cost increase. On the other hand, meso-fractures are hard to detect and properly describe in the seismic volumes, due to the spatial resolution of the dataset and to the capacity to detect these features using seismic waves. To fill the gap between well data and conventional seismic data, it has been developed a workflow named Tectonic Fractures Recognition and Characterization (T\Frac). The integration of borehole image logs, borehole data able to detect meso-scale features within the workflow can support a more reliable characterization.
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From Core to Sub-Seismic Scale Characterization, through Advanced Geosteering Technology; a Case Study
Authors F. Chinellato, M. Mele and R. MilanesiSummarySummary: Case study about multi-scale data interpretation to support reservoir understanding, sedimentological analysis and geosteering.
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Automated Interpretation in Borehole Geology While Drilling
Authors C. Shrivastava, K. Bondabou and F. VlietSummaryAutomation in borehole geology while drilling has taken long strides in past few years with advanced algorithms, computer vision and machine learning. Borehole images and drill-cuttings that form the backbone of well-centric geological interpretation have witness deployment of features extraction, properties estimation etc. with good success. For borehole images, the high angle and horizontal wells still pose challenge in even beds-picking with larger sinusoids associated uncertainties, and work is under advanced stage on that front. Drilling related features (breakout/ induced fractures) can be extracted, and fracture extraction is being attempted as well with varying degree of success. Sedimentary facies automated interpretation has been attempted successfully on borehole images. Drill-cuttings automated interpretation complements the borehole geology interpretation while drilling. This digital transformation of geology while drilling portfolio has started to yield benefits in quick and accessible automated interpretation available to various stakeholders as drilling commences and efficient well-delivery is achieved on expected lines.
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Investigating the Use of Machine Learning to Map Subsurface Channel Features
By E. AljishiSummaryUnsupervised seismic attributes clustering is a powerful tool that can expedite the overall interpretation and aid subsurface exploration. We explore in this example the use of Self-Organizing Map with variable input to map channel properties at the target. The automated workflow proposed replaces conventional quantitative and tedious workflows.
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