Incorporation of Bootstrapping and Cross-Validation for Efficient Multivariate Facies and Petrophysical Modeling

Al‐Mudhafar, Watheq J.

doi:10.2118/180277-ms

Cited by 27 publications

(7 citation statements)

References 15 publications

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“…The K-fold cross-validation helps in avoiding overfitting and estimating good results. 56 Figure 14 reflects the highest R 2 value and least MSE for optimized 10 folds, authenticating the outputs and validating the opted workflow performance. The performance of various algorithms was evaluated using statistical metrics such as MAE, RMSE, and R 2 , etc., as demonstrated in Figures 15 and 16, and it revealed that the ETR performed the best for predicting the volume of shale and effective porosities with a maximum correlation coefficient of 1.…”

Section: Petrophysical Interpretation-advanced Machine-learning Methodsmentioning

confidence: 56%

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Harnessing Advanced Machine-Learning Algorithms for Optimized Data Conditioning and Petrophysical Analysis of Heterogeneous, Thin Reservoirs

Manzoor

Ehsan

Hussain³

et al. 2023

Energy Fuels

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Petrophysical analysis is an industry-standard practice for reservoir evaluation as it provides critical inputs for characterizing subsurface formations and estimating resource potential. Khadro/Ranikot Formation sands are proliferous producers in the Central Indus Basin, Pakistan. The demarcate potential in intercalated sand shale layers that are thin and heterogeneous makes it a challenging reservoir. Conventional petrophysical interpretation is laborious and does not produce upto-mark results due to reservoir complexity, data limitations, and associated uncertainties. Hence, an emerging and delicate machinelearning (ML) approach has been comprehensively applied to analyze the potential and robustly interpret well log data while addressing the associated challenges. This case study entails a thorough evaluation of well log quality, assessing several algorithms such as least-squares support vector machines (one-class SVM), Random Forest Regressor (RFR), Extra Tree Regressor (ETR), Gradient Boosting Regressor (GBR), Decision Tree Classifier (DTC), etc. to compare their efficacy and reliability. One-class SVM helps to reduce outliers with great certainty, while the missing logs sonic (DT) and density (RHOB) are precisely predicted via GBR and ETR with 0.66 and 0.88 R 2 , respectively. Hence, providing reliable and optimized quality logs suitable for ML-based petrophysics. ML worked on these augmented logs by dividing the data into 60% training and 40% testing. The ETR outperformed the rest of the models with a correlation of 0.99 and 0.91 among conventional and ML results. Likewise, RFR performed exceptionally well for water saturation modeling, expressing the highest 0.93 correlation. Finally, DTC modeled reservoir facies with the best 91% accuracy and 0.935 F1 measures at the blind well. Excellent calibration of >85% is met with the estimates obtained by the predictive model compared to conventional methods. This comprehensive approach offers cost-effective and robust workarounds for modern formation evaluation with minimal uncertainty and resource-efficient multiwell interpretation within complex reservoirs and sets the stage for further research in the machine-learning ecosystem.

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Section: Petrophysical Interpretation-advanced Machine-learning Methodsmentioning

confidence: 56%

“…Their means reflect the score of algorithms, as mentioned before. The K-fold cross-validation helps in avoiding overfitting and estimating good results Figure reflects the highest R 2 value and least MSE for optimized 10 folds, authenticating the outputs and validating the opted workflow performance.…”

Section: Resultsmentioning

confidence: 99%

Harnessing Advanced Machine-Learning Algorithms for Optimized Data Conditioning and Petrophysical Analysis of Heterogeneous, Thin Reservoirs

Manzoor

Ehsan

Hussain³

et al. 2023

Energy Fuels

View full text Add to dashboard Cite

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“…The random sampling cross-validation was implemented on the entire experiment through sampling and dividing the given data set into two groups: 30% testing set for forecast and prediction while 70% training set for building the model. 43 More explicitly, the training subset was used as the base for cumulative oil production modeling, obtained by the reservoir simulator, as a function of operational parameters. The prediction is then utilized for testing subset data using the simulator as well as the proxy model.…”

Section: Proxy Modelingmentioning

confidence: 99%

“…Cross‐validation is necessary to maximize the opportunity of achieving global optima and to enhance the forecast precision of the proxy model. The random sampling cross‐validation was implemented on the entire experiment through sampling and dividing the given data set into two groups: 30% testing set for forecast and prediction while 70% training set for building the model 43 . More explicitly, the training subset was used as the base for cumulative oil production modeling, obtained by the reservoir simulator, as a function of operational parameters.…”

Section: Proxy Modelingmentioning

confidence: 99%

Rapid evaluation and optimization of carbon dioxide‐enhanced oil recovery using reduced‐physics proxy models

Al‐Mudhafar

Rao

Srinivasan

et al. 2022

Energy Science & Engineering

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The carbon dioxide (CO2) injection in oil reservoirs is a promising enhanced oil recovery method to reduce carbon emissions into the atmosphere. The CO2 injection simulation and optimization require a large computation time, especially in real large‐scale oil reservoirs. This paper integrates experimental design and machine learning to construct a reduced‐physics surrogate model alternative to the complex reservoir simulator. This surrogate model was used to evaluate and optimize the CO2‐assisted gravity drainage (CO2‐GAGD) process, applied to a clastic reservoir in the Rumaila oil field. In the GAGD process optimization, five operational decision parameters controlling the production and injection activities were manipulated to attain optimal future oil production. Hundreds of simulation runs were created by Latin Hypercube Sampling to build a proxy‐based optimization. The optimal scenario increased the cumulative oil production by 416 MMSTB at the end of 10‐year prediction period. Finally, four machine learning (ML) algorithms were built as proxy surrogates alternative to the complex compositional reservoir simulations: Quadratic Equation (QM), FUzzy logic‐GEnetic algorithm (FUzzy‐GEnetic), Multivariate Additive Regression Splines (MARS), and Generalized Boosted Modeling (GBM). To show how robust using the ML approaches for the fast CO2‐GAGD process optimization, the random subsampling cross‐validation was adopted to conclude the optimum proxy metamodel that provides the lowest mismatch between the proxy‐ and simulator‐based cumulative oil production. The GBM algorithm achieved the highest adjusted‐R2 (0.9973) and lowest root mean square prediction error MathClass-open( 3.704 × 1 0 6 MathClass-close) $(3.704\times 1{0}^{6})$ to produce the most accurate metamodel. The resulting accurate GBM proxy metamodel can be used for fast optimization of the CO2‐GAGD process. Specifically, GBM‐metamodel should lead to achieving higher optimal reservoir flow responses, especially with running a large number of simulation runs. Consequently, this proposed trained GBM‐ML proxy metamodel could be used for gas injection optimization and uncertainty assessment with far‐less computational effort than with the conventional approaches, which use a complex flow simulator.

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“…After obtaining the dataset, it is necessary to consider the division of the dataset for training, validation and testing [43]. A previous study shows different dataset partition has a certain influence on the training results and reasonable dataset partitioning can effectively improve the accuracy of the model [28].…”

Section: Machine Learning Training Designmentioning

confidence: 99%

Potential for Prediction of Water Saturation Distribution in Reservoirs Utilizing Machine Learning Methods

et al. 2019

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Machine learning technology is becoming increasingly prevalent in the petroleum industry, especially for reservoir characterization and drilling problems. The aim of this study is to present an alternative way to predict water saturation distribution in reservoirs with a machine learning method. In this study, we utilized Long Short-Term Memory (LSTM) to build a prediction model for forecast of water saturation distribution. The dataset deriving from monitoring and simulating of an actual reservoir was utilized for model training and testing. The data model after training was validated and utilized to forecast water saturation distribution, pressure distribution and oil production. We also compared standard Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) which are popular machine learning methods with LSTM for better water saturation prediction. The results show that the LSTM method has a good performance on the water saturation prediction with overall AARD below 14.82%. Compared with other machine learning methods such as GRU and standard RNN, LSTM has better performance in calculation accuracy. This study presented an alternative way for quick and robust prediction of water saturation distribution in reservoir.

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Incorporation of Bootstrapping and Cross-Validation for Efficient Multivariate Facies and Petrophysical Modeling

Cited by 27 publications

References 15 publications

Harnessing Advanced Machine-Learning Algorithms for Optimized Data Conditioning and Petrophysical Analysis of Heterogeneous, Thin Reservoirs

Harnessing Advanced Machine-Learning Algorithms for Optimized Data Conditioning and Petrophysical Analysis of Heterogeneous, Thin Reservoirs

Rapid evaluation and optimization of carbon dioxide‐enhanced oil recovery using reduced‐physics proxy models

Potential for Prediction of Water Saturation Distribution in Reservoirs Utilizing Machine Learning Methods

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