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Sixth EAGE Rock Physics Workshop
- Conference date: November 15-17, 2022
- Location: Riyadh, Saudi Arabia
- Published: 15 November 2022
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A practical application of synthetic logs derived from drilling parameters for geomechanical modeling and drilling optimization
Authors P. Golikov, R. Smith, A. Bakulin and O. De JesusSummaryThis case study applies machine learning to generate synthetic acoustic and density logs from gamma-ray and drilling parameters. We further derive various geomechanical properties from the synthetic logs and show how they can be utilized for drilling efficiency monitoring and drilling optimization.
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Machine learning approach for evaluating the cause of the overpressure zones in the tight carbonate reservoirs
Authors N. Bukhanov, P. Golikov and A. EgorovSummaryIn this paper, we present the semi-supervised approach based on seismic attributes analysis. We demonstrate that this purely data-driven approach can be used not only for the overpressure prediction but also for the validation of the hypothesis that overpressure zones can be indicated by a combination of seismic attributes.
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A new stochastic method for total organic carbon estimation using wireline log data
Authors M. Fawad and N.H. MondolSummaryThe total organic carbon (TOC) content of source rock shales is measured in a laboratory employing geochemical methods. These analyses are performed using cuttings or core samples acquired in a well. The geochemical data obtained is patchy and incomplete, depending on the sample availability. Therefore, the TOC information is often available in limited zones and absent where samples are not available or where the expensive laboratory analysis is not carried out. Issues of error in wellbore sample depths and contaminations may also result in inaccurate TOC representation. Therefore, the estimation of TOC all along a well is generally time-consuming, error-prone, and very expensive. TOC estimation using wireline log data has been suggested by several researchers. The present study introduces a new interactive, cost-effective, and reliable stochastic technique to estimate TOC using well log data. The suggested approach can be applied in any basin worldwide using suitable end-members and input parameter distributions in our proposed acoustic impedance-resistivity relation.
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Conventional Versus Deep Learned Shear Prediction Models: A Comparative Study on Thick Jurassic Carbonate Section
Authors S. Almeshari, A. Al Ghaithi and M. AhmedSummaryOn several occasions for developed fields, sonic data are not acquired in all drilled sections and need to be predicted using the known conventional approaches. Examples of these approaches are the empirical Greenberg-Castagna relation and theoretical rock physics models such as Xu-Payne. However, with recent advancement and exposure of several machine learning methods – we deployed our inhouse developed deep learned shear prediction method for a thick Jurassic carbonate sequence and compared with the traditional approaches to assess the results. In terms of workflow efficiency, the deep learned prediction method was several times faster than the conventional approaches, however good amount of time is spent initially to properly train the datasets and calibrate the machine learning parameters. Predicted log quality from all the methods yielded good match and usability for the seismic integrated workflows. Predictability itself was measured with the existing data to be either under or overestimated and correlation coefficients were computed to be in the same range.
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Novel Approach to Detect Low Resistivity Pay Using Elastic Attributes
By M. AshfaqSummaryResistivity logs are routinely used to identify the hydrocarbon-bearing zones in a well however certain phenomena such as the presence of clay minerals, micro-porosity, conductive minerals, fractures, etc., can affect the resistivity logs and will result in missing potential hydrocarbon zones during the drilling process. It also causes an inaccurate evaluation of pay zones leading to high uncertainty in the estimation of water saturation and net pay. Our proposed workflow uses the elastic attributes instead of resistivity logs to identify the low resistivity pay (LRP) zones. The workflow has been tested successfully in different exploration wells across carbonate and clastic reservoirs and the results have been verified by well tests and mud gases. Although the proposed workflow doesn’t require mud gases or formation testing results as prior information, they can be used (if available) to verify the results for pre-drilled wells.
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Impact of Seismic Petrophysics on Quantitative Seismic Interpretation Workflows: Lessons Learned and Recommendations
Authors N. Aldossary and M. AshfaqSummaryWe have demonstrated how seismic petrophysics plays a fundamental role in any successful quantitative seismic study. Overlooking even small variations in geophysical well logs will greatly impact the results and compromise the quality of the end product. We recommend to build a robust and consistent log conditioning workflow to prepare the best quality log data. Combining multi-disciplinary diagnostic plots at QC stage will greatly enhance the quality of the logs for quantitative seismic work.
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The role of calcite elastic moduli in rock physics.
By S. PayneSummaryThe elastic moduli of calcite are important parameters in constraining rock physics models in carbonate reservoirs. Reference values of elastic moduli exhibit a significant range, which results in uncertainty when using the models to interpret data. One approach for calibrating rock physics models is to estimate elastic moduli from the zero-porosity intercept of the dataset. Using an example wireline dataset from offshore Canada, this approach results in calcite mineral moduli that are towards the lower end of the distribution of reference values. The estimates from this approach can be considered effective elastic moduli that include compensation for other effects such as variations in mineralogy and the presence of micro-cracks. An alternative is to interpret micro-cracks in the formation to cause an offset between the zero-porosity intercept and the true elastic moduli of calcite. The later approach is demonstrated in this abstract using the Vernik-Kachanov model.
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Distinct Petro-acoustic signature for sonic-derived porosity prediction in dolostones
Authors M. Salih, A. El-Husseiny, J.J.G. Reijmer, H. Eltom and A. AbdelkarimSummaryDolomite reservoirs host significant volume of world’s hydrocarbons. Understanding the porosity distribution within such strata is crucial for hydrocarbon exploration. In this study, 100 dolomite samples were collected from Saudi Arabia to investigate the main controlling factors on sonic velocity, and to examine the application of porosity prediction from acoustic impedance (P-impedance).
Collected samples were plugged for porosity and velocity measurements. Thin-section were prepared from each sample to define the texture and pore types. Acoustic impedances were calculated using compressional velocity and bulk density.
The petrographic analysis revealed that the studied samples could be separated into two main groups Fabric-preserving dolostones, and Non-fabric preserving dolostones. In addition, pore type is the main controlling factor on the sonic velocity and consequently on the calculated acoustic impedances. The porosity-impedance relation reveals that the two groups could be separated at porosity higher than 10%. Each group showed distinct porosity-impedance trend characterized by high correlation (R2> 0.94). The fabric-preserving dolostones show higher impedances than non-fabric preserving dolostones. This is mainly due to the dominance of stiffer pores within the fabric-preserving dolostones, compared to less stiff pores within the non-fabric preserving dolostones. The results of this study can enhance the porosity prediction in dolostone strata.
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Applied Rock physics and Seismic Reservoir Characterization in Abu Dhabi: Synopsis of current challenges.
More LessSummaryRock physics and seismic reservoir characterization case studies from Abu Dhabi are presented. Such carbonate reservoir studies are particularly challenging due to the nature of the reservoir rocks and their high stiffness, and also due to the contamination of the near offset seismic data by remaining interbed multiples. However, improvements in seismic data quality, thanks to advances in acquisition and processing, combined with seismic inversion techniques, can lead to successful incorporation of seismic reservoir characterization models into the reservoir model building process. Examples of time-lapse feasibility studies with the key role of rock physics modelling are also provided.
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Petrophysical rock properties prediction from elastic properties using artificial neural network (ANN).
Authors V. Suleymanov, A. El-Husseiny and J. DvorkinSummaryThe interpretation of elastic rock properties into petrophysical properties is usually performed using deterministic rock physics and statistics-based approaches at the seismic scale. In this study, a machine learning workflow has been developed for predicting petrophysical rock properties such as porosity, mineralogy, and pore fluid from measured elastic properties in the well. In particular, the bulk density, P- and S-wave velocity logs were used as inputs to predict the rock properties. The workflow shows promising results in predicting, porosity, clay content, and water saturation with high accuracy.
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Rock Physics Case Studies of Western Canada Unconventional Plays
Authors A. Mustaqeem and V. BaranovaSummaryCanada has been the second largest unconventional oil and gas producer. The target rocks of unconventional shale/silt/carbonate plays span over 300 million years in geological time from Devonian to Cretaceous. Understanding the elastic parameters of these shale plays and comparing them to the existing production is important and have become critical for drilling sweet spots. Petrophysical and petrographic analysis provide a good insight to the lithology and clay content but does lack the understanding of geomechanics, stresses and fracture distribution. Elastic properties analysis of the well logs through Lambda-Rho vs Mu-Rho crossplots and other calibrated modulii allows interpreters to derive equivalent rock physics properties through pre-stack inversion. Three case studies will be presented starting from Devonian calcareous shale followed by Triassic deep water shales and ending with silty source rock shales of Cretaceous age. The goal of these studies is to understand the critical information that can be derived from well logs for successful commercial production.
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Contact line friction: overlooked dominant mechanism for seismic attenuation and velocity dispersion at partial saturation
By A. RozhkoSummaryIt is well-known that wave-induced porous fluid flow between heterogeneities of various scales is one of the major causes of seismic wave attenuation and velocity dispersion in porous media. This attenuation is caused by viscous friction between fluid molecules moving with different velocities in a bulk volume of flowing fluid inside a porous space. There is no friction between the fluid and solid due to Stoke’s no-slip boundary condition. However, this no-slip boundary condition is not applicable at the contact line location, i.e., at the location where the interface meniscus between immiscible fluids meets the solid’s surface. Fluid molecules can role and slip over the solid’s surface at the contact line location, causing attenuation of seismic wave energy by the contact line friction mechanism. Previous authors did not study this attenuation mechanism. In the recent series of publications, we have demonstrated that the contact line friction may dominate wave attenuation at the seismic frequencies in the partially saturated rock. Furthermore, we demonstrated that using this new physical phenomenon it is possible to explain several field and lab experiments that are difficult to explain using Biot-Gassmann’s theories.
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