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2nd EAGE Workshop on Quantifying Uncertainty in Depth Imaging
- Conference date: November 21-22, 2023
- Location: Kuala Lumpur, Malaysia
- Published: 21 November 2023
1 - 20 of 22 results
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Understanding Seismic Uncertainty at Pinhoe Field
More LessAuthors Y. Gong, S. Chen, L. Zhang, S. Yang, N. Seymour, M. Rive, T.R. P., C. Hoelting, D. Fell and R. BisleySummaryNot Provided
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Embracing the Depth Residual, a Pragmatic Approach to Depth Uncertainty
More LessSummaryThe accurate estimation of subsurface depths is vital in subsurface resource extraction, but the inherent uncertainty associated with depth measurements presents significant challenges. Herein, we propose a pragmatic solution to address the problem’s impact on decision-making, risk assessment, and resource evaluation.
Our work suggests a top-down depth calibration methodology using residuals generated from time to depth exercises involving velocities. By separating velocities into stratigraphic intervals and correcting them interval-by-interval, depth uncertainties can be derived for each layer. The method also considers the possibility of errors in the seismic interpretation.
We further discuss the estimation of depth uncertainty in undrilled regions using multiple calibrated velocity models and blind tests. By examining the residuals at excluded well locations, a 3D appreciation of depth uncertainty can be created.
In conclusion, our work acknowledges the challenge of accurately predicting depths outside known data regions but advocates for robust and reliable methods that utilize available information to create depth uncertainty models. As new wells are drilled and new well tops become available, there is an urgency to be integrated into the existing depth calibration workflow, providing more accurate ‘reference case’ depth maps for well planning and reserve estimates. The methods described herein have been proven in practice by drilling wells in areas of lower well densities. The depth outcomes based on the depth uncertainty study have been shown to be within the ranges calculated by these methods.
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Adaptive Learning Inversion with Gaussian Uncertainty
More LessAuthors E. Sandoval Curiel, D. Colombo, E. Turkoglu and M. ChararaSummaryAn adaptive learning inversion scheme was developed for geophysical data based on Gaussian Process and Active Learning, which goal is the reduction of the pool of data for training. The adaptive, physics-driven learning process uses evidence from field data as generated by an inversion process to provide real-world proxies to the machine learning training and ensure optimal generalization for field data applications. The dynamic learning model is obtained by an iterative feedback loop between the inversion process and an AI system progressively adapting to the characteristics of the field data. Convergence metrics is developed to monitor the flattening of the learning curve while ensuring the convergence of the data misfit from the physics-based procedure. The developed workflow enhances the generalization properties of machine learning for field data applications while ensuring only a small and statistically selected dataset are used for the task. The developed approach is tested on helicopter-borne transient EM and on seismic Full Waveform Inversion.
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Post- vs Pre-Migration Residual Velocity Analysis: a Case Study in the Gulf of Thailand
More LessAuthors C.K. Lim, E. Sulaeman, A. Chansane, K. Wangkawong, N.P. Phantawee, N. Laoniyomthai and P. NampratchayakulSummaryPost-migration residual velocity analysis is part of routine process in standard processing sequence for both time and depth imaging. However, decision either to proceed with post-migration residual velocity or adding another iteration of velocity update prior to migration can be a challenging issue for such as tight turnaround project or due to poor velocity control from inaccurate well information. In this case, it is often to wrap up the modelling process even before adequately reaching a minimum level of residuals and let post-migration process to handle. Managing unoptimized model after migration can be harmful for seismic image quality as the approach completely disregards migration involvement in flattening the gathers and tying to the wells. Post-migration residual correction approach covering gather flattening and depthing, is a 1D solution and therefore, to a certain degree, helpless to recover any false image associated with suboptimal 3D imaging process. Thus, when the total residual corrections from post-migration gather flattening and depthing are numerically significant, then pre-migration velocity upgrade should be considered.
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Uncertainty of DHI - Case Study
More LessAuthors N. Chacon-Buitrago, M. Belonosov, F. Jiang and K. OsypovSummaryNot Provided
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How Thin is a Thin Bed? an Uncertainty Perspective
More LessAuthors K. Torres, M. Belonosov, F. Jiang and K. OsypovSummaryNot Provided
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A Hybrid Approach to Caprock Potential Assessment and Uncertainty Estimation: Bagging SVM and Random Forest
More LessSummaryCaprock integrity is important for CO2 storage to prevent leaks and failures. Accurate and systematic assessment of the caprock’s sealing ability, capacity, and shape under increasing confining pressures can identify risk factors and help mitigate leakage risk effectively. Laboratory measurements of core samples provide critical data on the responses of the caprock to pressure and changes in mechanical properties. Parameters from various tests and measurements namely, Triaxial compression, Ultrasonic velocity, In-situ stress deformation, and Mohr-Coulomb Failure envelope allowed rapid analysis of caprock parameters. Evaluation of caprock conditions using 21 collected records on 17 features using Random Forest algorithm for feature selection was attempted in this study. Caprock potential prediction was done using Bagging SVM classifiers with authentic labels, based on Low to High risk entities. Uncertainty estimation evaluated the outcomes and validated the predictions.
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