Reservoir Uncertainty Analysis: The Trends from Probability to Algorithms and Machine Learning

Ani, Maureen; Oluyemi, Gbenga; Petrovski, Andrei; Gomari, Sina Rezaei

doi:10.2118/181049-ms

Cited by 14 publications

(4 citation statements)

References 40 publications

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“…Focusing on the uncertainty estimation, the literature concerning the application of these methodologies to real-life problems is quite vast; it ranges from chemistry [11,12] to material science [13], localization [14], geology [15], and, of course, medical science [16], where the majority of the work employed techniques based on bootstrapping, Bayesian approaches, and dropouts.…”

Section: Previous Workmentioning

confidence: 99%

Uncertainty Estimation for Machine Learning Models in Multiphase Flow Applications

et al. 2021

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In oil and gas production, it is essential to monitor some performance indicators that are related to the composition of the extracted mixture, such as the liquid and gas content of the flow. These indicators cannot be directly measured and must be inferred with other measurements by using soft sensor approaches that model the target quantity. For the purpose of production monitoring, point estimation alone is not enough, and a confidence interval is required in order to assess the uncertainty in the provided measure. Decisions based on these estimations can have a large impact on production costs; therefore, providing a quantification of uncertainty can help operators make the most correct choices. This paper focuses on the estimation of the performance indicator called the water-in-liquid ratio by using data-driven tools: firstly, anomaly detection techniques are employed to find data that can alter the performance of the subsequent model; then, different machine learning models, such as Gaussian processes, random forests, linear local forests, and neural networks, are tested and employed to perform uncertainty-aware predictions on data coming from an industrial tool, the multiphase flow meter, which collects multiple signals from the flow mixture. The reported results show the differences between the discussed approaches and the advantages of the uncertainty estimation; in particular, they show that methods such as the Gaussian process and linear local forest are capable of reaching competitive performance in terms of both RMSE (1.9–2.1) and estimated uncertainty (1.6–2.6).

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Section: Previous Workmentioning

confidence: 99%

Uncertainty Estimation for Machine Learning Models in Multiphase Flow Applications

et al. 2021

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“…Another critical task is analysing and predicting geological parameters, such as Lithology, Porosity, Depositional Environment. This task can be solved using artificial neural networks, support vector machines, classification and regression models [4]. It was shown that machine learning methods could infer domain knowledge from well log data and identify the most important parameters in reservoir analogues data.…”

Section: Introductionmentioning

confidence: 99%

Oil reservoir recovery factor assessment using Bayesian networks based on advanced approaches to analogues clustering

Andriushchenko¹,

Deeva²,

Bubnova³

et al. 2022

Preprint

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The work focuses on the modelling and imputation of oil and gas reservoirs parameters, specifically, the problem of predicting the oil recovery factor (RF) using Bayesian networks (BNs). Recovery forecasting is critical for the oil and gas industry as it directly affects a company's profit. However, current approaches to forecasting the RF are complex and computationally expensive. In addition, they require vast amount of data and are difficult to constrain in the early stages of reservoir development. To address this problem, we propose a BN approach and describe ways to improve parameter predictions' accuracy. Various training hyperparameters for BNs were considered, and the best ones were used. The approaches of structure and parameter learning, data discretization and normalization, subsampling on analogues of the target reservoir, clustering of networks and data filtering were considered. Finally, a physical model of a synthetic oil reservoir was used to validate BNs' predictions of the RF. All approaches to modelling based on BNs provide full coverage of the confidence interval for the RF predicted by the physical model, but at the same time require less time and data for modelling, which demonstrates the possibility of using in the early stages of reservoirs development. The main result of the work can be considered the development of a methodology for studying the parameters of reservoirs based on Bayesian networks built on small amounts of data and with minimal involvement of expert knowledge. The methodology was tested on the example of the problem of the recovery factor imputation.

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“…The AI technology exhibits promising potentials to assist and improve the conventional reservoir engineering approaches in a large spectrum of reservoir engineering problems [1][2][3][4]. Advanced machine-learning algorithms such as fuzzy logic (FL), artificial neural networks (ANN), support vector machines (SVM), response surface model (RSM) are employed by numerous studies as regression and classification tools [5][6][7][8]. Most of the machine-learning algorithms used in the reservoir engineering area belong to the category of supervised learning.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Applications in Reservoir Engineering: A Status Check

Ertekin

Sun

2019

Energies

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This article provides a comprehensive review of the state-of-art in the area of artificial intelligence applications to solve reservoir engineering problems. Research works including proxy model development, artificial-intelligence-assisted history-matching, project design, and optimization, etc. are presented to demonstrate the robustness of the intelligence systems. The successes of the developments prove the advantages of the AI approaches in terms of high computational efficacy and strong learning capabilities. Thus, the implementation of intelligence models enables reservoir engineers to accomplish many challenging and time-intensive works more effectively. However, it is not yet astute to completely replace the conventional reservoir engineering models with intelligent systems, since the defects of the technology cannot be ignored. The trend of research and industrial practices of reservoir engineering area would be establishing a hand-shaking protocol between the conventional modeling and the intelligent systems. Taking advantages of both methods, more robust solutions could be obtained with significantly less computational overheads.

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Reservoir Uncertainty Analysis: The Trends from Probability to Algorithms and Machine Learning

Cited by 14 publications

References 40 publications

Uncertainty Estimation for Machine Learning Models in Multiphase Flow Applications

Uncertainty Estimation for Machine Learning Models in Multiphase Flow Applications

Oil reservoir recovery factor assessment using Bayesian networks based on advanced approaches to analogues clustering

Artificial Intelligence Applications in Reservoir Engineering: A Status Check

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