A novel methodology for fast reservoir simulation of single-phase gas reservoirs using machine learning

Bhattacharyya, Subhrajyoti; Vyas, Aditya

doi:10.1016/j.heliyon.2022.e12067

Cited by 9 publications

(3 citation statements)

References 18 publications

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“…Meanwhile, many scholars have also used machine learning algorithms for reservoir simulation studies in the field of oil and gas field development in recent years. Bhattacharyya et al (2022aBhattacharyya et al ( , 2022bBhattacharyya et al ( , 2022c) developed a novel data-driven-based model that could accurately predict the decline curves and EUR (Estimated Ultimate Recovery) for new wells based on the data collected from nearby wells and this paper presented use of new algorithm as well as a new dataset. Bhattacharyya et al (2022aBhattacharyya et al ( , 2022bBhattacharyya et al ( , 2022c) developed an innovative machine learning (ML) (random forest (RF)) based model for fast rate-decline and EUR prediction in Bakken Shale oil wells.…”

Section: Introductionmentioning

confidence: 99%

“…Bhattacharyya et al (2022aBhattacharyya et al ( , 2022bBhattacharyya et al ( , 2022c) developed a novel data-driven-based model that could accurately predict the decline curves and EUR (Estimated Ultimate Recovery) for new wells based on the data collected from nearby wells and this paper presented use of new algorithm as well as a new dataset. Bhattacharyya et al (2022aBhattacharyya et al ( , 2022bBhattacharyya et al ( , 2022c) developed an innovative machine learning (ML) (random forest (RF)) based model for fast rate-decline and EUR prediction in Bakken Shale oil wells. Bhattacharyya et al (2022aBhattacharyya et al ( , 2022bBhattacharyya et al ( , 2022c presented a novel approach for reservoir simulation that used Random Forest (RF) which is one of the widely used Machine learning (ML) algorithm to reduce the number of iterations at each time step and speed up the simulation process.…”

Section: Introductionmentioning

confidence: 99%

“…Bhattacharyya et al (2022aBhattacharyya et al ( , 2022bBhattacharyya et al ( , 2022c) developed an innovative machine learning (ML) (random forest (RF)) based model for fast rate-decline and EUR prediction in Bakken Shale oil wells. Bhattacharyya et al (2022aBhattacharyya et al ( , 2022bBhattacharyya et al ( , 2022c presented a novel approach for reservoir simulation that used Random Forest (RF) which is one of the widely used Machine learning (ML) algorithm to reduce the number of iterations at each time step and speed up the simulation process. The study's novelty was in developing a new ML-based reservoir simulator that would make reservoir simulation much faster and computationally more efficient.…”

Section: Introductionmentioning

confidence: 99%

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Real-time prediction of multivariate ROP (rate of penetration) based on machine learning regression algorithms: Algorithm comparison, model evaluation and parameter analysis

Liu

Tang

et al. 2023

Energy Exploration & Exploitation

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ROP (Rate of Penetration) is a comprehensive indicator of the rock drilling process and how efficiently predicting drilling rates is important to optimize resource allocation, reduce drilling costs and manage drilling hazards. However, the traditional model is difficult to consider the multiple factors, which makes the prediction accuracy difficult to meet the real drilling requirements. In order to provide efficient, accurate and comprehensive information for drilling operation decision-making, this study evaluated the applicability of four typical regression algorithms based on machine learning for predicting pore pressure in Troll West field, namely SVR (Support Vector Regression), Linear regression, Regression Tree and Gradient Boosting regression. These methods allow more parameters input. By comparing the prediction results of these typical regression algorithms based on R2(R-Square), explained variance, mean absolute error, mean squared error, median absolute error and other performance indicators, it was found that each method predicted different results, among which Gradient Boosting regression has the best results, their prediction accuracy is high and the error is very low. The prediction accuracy of these methods is positively correlated with the proportion of the training data set. With the increase of logging features, the prediction accuracy is gradually improved. In the prediction of adjacent wells, the ROP prediction methods can achieve a certain prediction effect, which shows that this method is suitable for ROP prediction in Troll West field.

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