Application of Grey Theory for Oil and Gas Reservoir Evaluation Program Optimization

Zhang, Zai Xu; Kang, Xiao Feng; Zhang, Fei Fei

doi:10.4028/www.scientific.net/amr.616-618.1008

Cited by 6 publications

(4 citation statements)

References 1 publication

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“…These three ML techniques were Decision Trees (DT), Random Forest Regressor (RF), and K-Nearest Neighbor (KNN). Machine learning and artificial intelligence is making great progress in the oil and gas industry, with various researchers investigating advance applications pertaining to complex/heterogenous systems (Konoshonkin et al, 2020;Ma et al, 2018;Mohaghegh et al, 1994;wu zy and liu, 2018;Zhang et al, 2005Zhang et al, , 2012 (Elkatatny et al, 2016;Tariq, 2018;Tariq et al, 2021Tariq et al, , 2020cTariq et al, , 2020dTariq et al, , 2019aTariq et al, , 2019bTariq and Mahmoud, 2019). A wide range of ML algorithms have been employed to develop models/correlations for estimating various parameters related to hydrocarbons development (Ahmadi et al, 2014;Anifowose et al, 2015;da Silva et al, 2005;Janjua et al, 2016;Khan et al, 2019aKhan et al, , 2019bKhan et al, , 2018bKhan et al, , 2018aKhan et al, , 2018cLi et al, 2020;Tariq et al, 2018Tariq et al, , 2016Tohidi-Hosseini et al, 2016).…”

Section: Methodsmentioning

confidence: 99%

A Machine Learning Based Accelerated Approach to Infer the Breakdown Pressure of the Tight Rocks

Tariq

Yan

Sun

et al. 2022

Day 2 Tue, November 01, 2022

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Unconventional oil reservoirs are usually classified by extremely low porosity and permeability values. The most economical way to produce hydrocarbons from such reservoirs is by creating artificially induced fractures. To design the hydraulic fracturing jobs, true values of rock breakdown pressure is required. Conducting hydraulic fracturing experiments in the laboratory is a very expensive and time consuming process. Therefore, in this study, different machine learning models were efficiently utilized to predict the breakdown pressure of the tight rocks. In the first part of the study, a comprehensive hydraulic fracturing experimental study was conducted on various rock specimens, to measure the breakdown pressure. A total of 130 experiments were conducted on different rock types such as shales, sandstone, tight carbonates, and synthetic cement samples. Rock mechanical properties such as Young's Modulus E, Poisson's ratio, Unconfined Compressive strength (UCS), and indirect tensile strength sigma_t were measured before conducting hydraulic fracturing tests. Machine learning models were used to correlate the breakdown pressure of the rock as a function of fracturing experimental conditions and rock properties. In the machine learning model, we considered experimental conditions including injection rate, overburden pressures, and fracturing fluid viscosity, and rock properties including Young's Modulus, Poisson's ratio, Unconfined Compressive strength (UCS), and indirect tensile strength, porosity, permeability, and bulk density. Machine learning models include Random Forest (RF), Decision Trees (DT), and K-Nearest Neighbor (KNN). During training of ML models, the model hyper-parameters were optimized by grid search optimization approach. With the optimal setting of the ML models, the breakdown pressure of the unconventional formation were predicted with an accuracy of 95%. The proposed methodology to predict the breakdown pressure of unconventional rocks can minimize the laboratory experimental cost of measuring fracture parameters and can be used as a quick assessment tool to evaluate the development prospect of unconventional tight rocks.

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Section: Methodsmentioning

confidence: 99%

A Machine Learning Based Accelerated Approach to Infer the Breakdown Pressure of the Tight Rocks

Tariq

Yan

Sun

et al. 2022

Day 2 Tue, November 01, 2022

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“…The asset optimization process involves utilizing appropriate evaluation methods to optimally select oil and gas assets, with the development of disciplines such as probability theory, mathematical programming, fuzzy mathematics, and multi-attribute evaluation. A plethora of methods have been employed by previous researchers for project evaluation in the oil and gas field, such as gray theory [6,7], grey fuzzy that combines grey theory with fuzzy evaluation [8], cyclical convolution [9], Multi-Attribute Decision Making (MADM) in conjunction with Analytic Hierarchy Process (AHP) [10], Multi-Criteria Decision Analysis (MCDA) [11], as well as the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Fuzzy-TOPSIS [12,13].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Asset Evaluation Method for Oil and Gas Projects

et al. 2023

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The rapid and accurate evaluation of oil and gas assets, specifically for new development projects, poses a significant challenge due to the various project types, limited data availability, brief periods for assessment and decision making, and constraints arising from varying contractual and taxation conditions, political stability, and societal factors. This study leverages the grading standards of the evaluation index system for new oil and gas field development projects, along with relevant mathematical theories and methods for project evaluation and optimization. We developed an asset evaluation approach for new oil and gas projects by analyzing the assets of six new oil and gas field development projects in Brazil. This analysis resulted in the grading and ranking of new projects, and we tested and demonstrated four asset optimization techniques. After a comparative analysis with conventional evaluation results, we established an oil and gas project asset optimization approach centered on the cloud model comprehensive evaluation and linear weighted ranking, exhibiting Kendall’s tau coefficient of 0.8667 with conventional methods. The findings suggest that the combination of the cloud model comprehensive evaluation method with the linear weighted ranking method can facilitate asset optimization for oil and gas field development projects, meeting the practical needs for fast selection among various new projects. Furthermore, this research offers a technical and theoretical foundation for rapid evaluation and decision making regarding new assets.

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“…Machine learning is making great progress in the oil and gas industry, with various researchers investigating advance applications pertaining to complex/heterogenous systems (Konoshonkin et al, 2020;Ma et al, 2018;Mohaghegh et al, 1994;wu zy and liu, 2018;Zhang et al, 2005Zhang et al, , 2012 (Elkatatny et al, 2016;Tariq, 2018;Tariq et al, 2021Tariq et al, , 2019aTariq et al, , 2019bZeeshan Tariq et al, 2020b, 2020cTariq and Mahmoud, 2019). A wide range of AI algorithms have been employed to develop models/correlations for estimating various parameters related to hydrocarbons development (Ahmadi et al, 2014;Anifowose et al, 2015;da Silva et al, 2005;Janjua et al, 2016;Khan et al, 2019aKhan et al, , 2019bKhan et al, , 2018bKhan et al, , 2018aKhan et al, , 2018cLi et al, 2020;Tariq et al, 2018Tariq et al, , 2016Tohidi-Hosseini et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

An Effective Method of Estimating Nuclear Magnetic Resonance Based Porosity Using Deep Learning Approach

Tariq

Gudala

et al. 2022

Day 3 Wed, November 02, 2022

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Carbonate rocks are very heterogeneous and have very complex pores structure due to the presence of intra-particle and inter-particle porosities. This makes the characterization and evaluation of the petrophysical data, and the interpretation of the carbonate rocks a big challenge. Porosity in complex lithologies, particularly carbonate reservoirs, is difficult to measure using conventional (Quad-Combo) well logs. Nuclear Magnetic Resonance (NMR) derived porosity is considered the total porosity "gold standard", as it is measured exclusive of matrix and mineralogy. However, due to NMR tools existing as relatively new technology, and the extra expense in logging runs and rig time, most wells lack these data. Most of the existing approaches to predict the rock porosity was developed on the Neutron-density porosity logs that usually are resulted in inaccurate estimation, especially in the fractured zone and highly dolomitized rocks. In this study, deep learning model was efficiently utilized to predict the Nuclear Magnetic Resonance based effective porosity in carbonate rocks. The petrophysical well logs such as bulk density, gamma-ray, neutron porosity, photoelectric log, and caliper log were used as predictors. A total of 3800 data points were obtained from several wells located in a carbonate reservoir. A comprehensive data exploratory analysis tools (EDA) was utilized to evaluate the quality of the dataset which led to removing the extreme values and outliers. A fully connected Deep Neural Network (DNN) was trained to predict NMR based effective porosity. The hyperparameters of DNN model such as number of hidden layers, number of neurons, activation functions, and learning algorithms were varied using a grid search optimization approach. The K-fold cross-validation criteria were used to enhance the generalization capabilities of ML models. The evaluation of ML models was assessed by the coefficient of determination (R2), root means square error (RMSE), and. average absolute percentage error (AAPE). The results showed that the DNN resulted in a significantly low error and high R2 between actual and predicted values. An accuracy of 87% was recorded between actual and predicted NMR values. The new model to predict the NMR porosity is trained on the NMR-determined porosity. NMR porosity is based on the number of hydrogen nuclei in the pore spaces that are independent of the rock minerals and related to the pore spaces only.

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Application of Grey Theory for Oil and Gas Reservoir Evaluation Program Optimization

Cited by 6 publications

References 1 publication

A Machine Learning Based Accelerated Approach to Infer the Breakdown Pressure of the Tight Rocks

A Machine Learning Based Accelerated Approach to Infer the Breakdown Pressure of the Tight Rocks

A Comprehensive Asset Evaluation Method for Oil and Gas Projects

An Effective Method of Estimating Nuclear Magnetic Resonance Based Porosity Using Deep Learning Approach

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