Using Machine Learning to Reduce Ensembles of Geological Models for Oil and Gas Exploration

Roubickova, Anna; Brown, Nick; Brown, Oliver Thomson

doi:10.1109/drbsd-549595.2019.00012

Cited by 4 publications

(6 citation statements)

References 6 publications

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“…The application of Random Forest in petroleum exploration has been gaining traction in recent years. For instance, Roubícková et al [48] utilized Random Forest to reduce ensembles of geological models for oil and gas exploration. They found that the Random Forest algorithm effectively identifies the critical grouping of models based on the most essential feature.…”

Section: Random Forestmentioning

confidence: 99%

Post-Fracture Production Prediction with Production Segmentation and Well Logging: Harnessing Pipelines and Hyperparameter Tuning with GridSearchCV

Sun,

Wang,

Wang

et al. 2024

Applied Sciences

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As the petroleum industry increasingly exploits unconventional reservoirs with low permeability and porosity, accurate predictions of post-fracture production are becoming critical for investment decisions, energy policy development, and environmental impact assessments. However, despite extensive research, accurately forecasting post-fracture production using well-log data continues to be a complex challenge. This study introduces a new method of data volume expansion, which is to subdivide the gas production of each well on the first day according to the depth of logging data, and to rely on the correlation model between petrophysical parameters and gas production to accurately combine the gas production data while matching the accuracy of the well-log data. Twelve pipelines were constructed utilizing a range of techniques to fit the regression relationship between logging parameters and post-fracture gas production These included data preprocessing methods (StandardScaler and RobustScaler), feature extraction approaches (PCA and PolynomialFeatures), and advanced machine learning models (XGBoost, Random Forest, and neural networks). Hyperparameter optimization was executed via GridSearchCV. To assess the efficacy of diverse models, metrics including the coefficient of determination (R2), standard deviation (SD), Pearson correlation coefficient (PCC), mean absolute error (MAE), mean squared error (MSE), and root-mean-square error (RMSE) were invoked. Among the several pipelines explored, the PFS-NN exhibited excellent predictive capability in specific reservoir contexts. In essence, integrating machine learning with logging parameters can be used to effectively assess reservoir productivity at multi-meter formation scales. This strategy not only mitigates uncertainties endemic to reservoir exploration but also equips petroleum engineers with the ability to monitor reservoir dynamics, thereby facilitating reservoir development. Additionally, this approach provides reservoir engineers with an efficient means of reservoir performance oversight.

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Section: Random Forestmentioning

confidence: 99%

Post-Fracture Production Prediction with Production Segmentation and Well Logging: Harnessing Pipelines and Hyperparameter Tuning with GridSearchCV

Sun,

Wang,

Wang

et al. 2024

Applied Sciences

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“…Examples of machine learning utilization are petroleum exploration and production forecasting [53], [59], detection and correction of equipment malfunctions [72], maintenance support system [33], reservoir modeling and characterization [21], [31] and drilling performance optimization [30], [63].…”

Section: Machine Learning In Oil and Gasmentioning

confidence: 99%

“…In determining locations to develop oil fields takes significant effort to manually process and interpret well log data [60]. Many machine learning-driven approaches have been proposed to address the lengthy and time-consuming issue [10], [32], [53], [60]. However, none had succeeded in a fully automated process without human intervention in interpreting and concluding the results [10], [53], [60].…”

Section: Machine Learning In Oil and Gasmentioning

confidence: 99%

“…Many machine learning-driven approaches have been proposed to address the lengthy and time-consuming issue [10], [32], [53], [60]. However, none had succeeded in a fully automated process without human intervention in interpreting and concluding the results [10], [53], [60]. The significant amount of missing data in well logs is one of the key hindrances in applying machine learning [25].…”

Section: Machine Learning In Oil and Gasmentioning

confidence: 99%

“…Geological model matching is another tedious task in field exploration. Roubickova et al [53] proposed a semi-supervised clustering approach to significantly reduce a number of models used in determining locations for developing oil fields. Firstly, regression analysis was used to estimate the amount of oil in place (OIP) in each geological model.…”

Section: Machine Learning In Oil and Gasmentioning

confidence: 99%

See 2 more Smart Citations

A Data-Driven Decision Support Tool for Offshore Oil and Gas Decommissioning

Vuttipittayamongkol¹,

Tung

Elyan

2021

IEEE Access

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A growing number of oil and gas offshore infrastructures across the globe are approaching the end of their operational life. It is a major challenge for the industry to plan and make a decision on the decommissioning as the processes are resource exhaustive. Whether a facility is completely removed, partially removed or left in-situ, each option will affect individual parties differently. Stakeholders' concerns and needs are collected and analyzed to obtain the most compromised decommissioning decision. Engaging with hundreds of stakeholders is extremely complicated, hence time-consuming and costly. This issue can be addressed using a predictive model to provide suggested decommissioning options based on the data of previously approved projects. However, the lack of readily available relevant datasets is the main hindrance of such an approach. In this paper, we introduce a new oil and gas decommissioning dataset extensively covering all types of offshore infrastructures in the UK landscape over a 21-year period. An experimental framework using several learning algorithms on the new dataset for predicting the decommissioning option is presented. Various resampling methods were applied to tackle the imbalanced class distribution of the dataset for improved classification. Promising results were achieved despite the exclusion of some stakeholder-related features used in the traditional approach. This shows signs of a potential solution for the industry to significantly reduce time and cost spent on a decommissioning project, and encourages more efforts put into researching on this timely topic.

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Identifying payable cluster distributions for improved reservoir characterization: a robust unsupervised ML strategy for rock typing of depositional facies in heterogeneous rocks

Ashraf,

Anees,

Zhang

et al. 2024

Geomech. Geophys. Geo-energ. Geo-resour.

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The oil and gas industry relies on accurately predicting profitable clusters in subsurface formations for geophysical reservoir analysis. It is challenging to predict payable clusters in complicated geological settings like the Lower Indus Basin, Pakistan. In complex, high-dimensional heterogeneous geological settings, traditional statistical methods seldom provide correct results. Therefore, this paper introduces a robust unsupervised AI strategy designed to identify and classify profitable zones using self-organizing maps (SOM) and K-means clustering techniques. Results of SOM and K-means clustering provided the reservoir potentials of six depositional facies types (MBSD, DCSD, MBSMD, SSiCL, SMDFM, MBSh) based on cluster distributions. The depositional facies MBSD and DCSD exhibited high similarity and achieved a maximum effective porosity (PHIE) value of ≥ 15%, indicating good reservoir rock typing (RRT) features. The density-based spatial clustering of applications with noise (DBSCAN) showed minimum outliers through meta cluster attributes and confirmed the reliability of the generated cluster results. Shapley Additive Explanations (SHAP) model identified PHIE as the most significant parameter and was beneficial in identifying payable and non-payable clustering zones. Additionally, this strategy highlights the importance of unsupervised AI in managing profitable cluster distribution across various geological formations, going beyond simple reservoir characterization.

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Using Machine Learning to Reduce Ensembles of Geological Models for Oil and Gas Exploration

Cited by 4 publications

References 6 publications

Post-Fracture Production Prediction with Production Segmentation and Well Logging: Harnessing Pipelines and Hyperparameter Tuning with GridSearchCV

Post-Fracture Production Prediction with Production Segmentation and Well Logging: Harnessing Pipelines and Hyperparameter Tuning with GridSearchCV

A Data-Driven Decision Support Tool for Offshore Oil and Gas Decommissioning

Identifying payable cluster distributions for improved reservoir characterization: a robust unsupervised ML strategy for rock typing of depositional facies in heterogeneous rocks

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