Energies

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27 pages, 3134 KiB

Open AccessArticle

Parallel Automatic History Matching Algorithm Using Reinforcement Learning

by Omar S. Alolayan, Abdullah O. Alomar and John R. Williams

Energies 2023, 16(2), 860; https://doi.org/10.3390/en16020860 - 12 Jan 2023

Cited by 4 | Viewed by 2297

Reformulating the history matching problem from a least-square mathematical optimization problem into a Markov Decision Process introduces a method in which reinforcement learning can be utilized to solve the problem. This method provides a mechanism where an artificial deep neural network agent can [...] Read more.

Reformulating the history matching problem from a least-square mathematical optimization problem into a Markov Decision Process introduces a method in which reinforcement learning can be utilized to solve the problem. This method provides a mechanism where an artificial deep neural network agent can interact with the reservoir simulator and find multiple different solutions to the problem. Such a formulation allows for solving the problem in parallel by launching multiple concurrent environments enabling the agent to learn simultaneously from all the environments at once, achieving significant speed up. Full article

(This article belongs to the Special Issue AI Technologies in Oil and Gas Geological Engineering)

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14 pages, 6964 KiB

Open AccessArticle

Identification of Water Flooding Advantage Seepage Channels Based on Meta-Learning

by Chi Dong, Baobin Zhang, Erlong Yang, Jinhao Lu and Linmo Zhang

Energies 2023, 16(2), 687; https://doi.org/10.3390/en16020687 - 6 Jan 2023

Cited by 2 | Viewed by 2296

Abstract

As the water injection oilfield enters into the high water cut stage, a large number of water flooding advantage seepage channels are formed in the local reservoir dynamically changing with the water injection process, which seriously affects the water injection development effect. In [...] Read more.

As the water injection oilfield enters into the high water cut stage, a large number of water flooding advantage seepage channels are formed in the local reservoir dynamically changing with the water injection process, which seriously affects the water injection development effect. In oilfield production, water injection and fluid production profile test data are direct evidence to identify advantage seepage channels. In recent years, some scholars have carried out research related to the identification of advantage seepage channels based on machine learning methods; however, the insufficient profile test data limit the quantity and quality of learning samples, leading to problems such as low prediction accuracy of learning models. Therefore, the author proposes a new method of advantage seepage channel identification based on meta-learning techniques, using the MAML algorithm to optimize the neural network model so that the model can still perform well in the face of training tasks with low data sample size and low data quality. Finally, the model was applied to the actual blocks in the field to identify the advantage seepage channels, and the identification results were basically consistent with the tracer monitoring results, which confirmed the feasibility of the method. It provides a new solution idea for the task of identifying advantage seepage channels and other tasks with low data quality, which has a certain guiding significance. Full article

(This article belongs to the Special Issue AI Technologies in Oil and Gas Geological Engineering)

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21 pages, 10019 KiB

Open AccessArticle

Re-Evaluation of Oil Bearing for Wells with Long Production Histories in Low Permeability Reservoirs Using Data-Driven Models

by Yongchao Xue, Chong Cao, Qingshuang Jin and Qianyu Wang

Energies 2023, 16(2), 677; https://doi.org/10.3390/en16020677 - 6 Jan 2023

Viewed by 1432

Abstract

The re-evaluation of oil-bearing wells enables finding potential oil-bearing areas and estimating the results of well logging. The re-evaluation of oil bearing is one of the key procedures for guiding the development of lower production wells with long-term production histories. However, there are [...] Read more.

The re-evaluation of oil-bearing wells enables finding potential oil-bearing areas and estimating the results of well logging. The re-evaluation of oil bearing is one of the key procedures for guiding the development of lower production wells with long-term production histories. However, there are many limitations to traditional oil-bearing assessment due to low resolution and excessive reliance on geological expert experience, which may lead to inaccurate and uncertain predictions. Based on information gain, three data-driven models were established in this paper to re-evaluate the oil bearing of long-term production wells. The results indicated that the RF model performed best with an accuracy of 95.07%, while the prediction capability of the neural network model was the worst, with only 79.8% accuracy. Moreover, an integrated model was explored to improve model accuracy. Compared with the neural network, support vector machine, and random forest models, the accuracy of the fusion model was improved by 20.9%, 8.5%, and 1.4%, which indicated that the integrated model assisted in enhancing the accuracy of oil-bearing prediction. Combined with the long-term production characteristics of oil wells in the actual oil field, the potential target sweet spot was found, providing theoretical guidance for the effective development of lower production wells in the late period of oilfield development. Full article

(This article belongs to the Special Issue AI Technologies in Oil and Gas Geological Engineering)

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11 pages, 2936 KiB

Open AccessArticle

Predicting Scale Thickness in Oil Pipelines Using Frequency Characteristics and an Artificial Neural Network in a Stratified Flow Regime

by Tzu-Chia Chen, Abdullah M. Iliyasu, Robert Hanus, Ahmed S. Salama and Kaoru Hirota

Energies 2022, 15(20), 7564; https://doi.org/10.3390/en15207564 - 13 Oct 2022

Cited by 2 | Viewed by 1472

Abstract

One of the main problems in oil fields is the deposition of scale inside oil pipelines, which causes problems such as the reduction of the internal diameter of oil pipes, the need for more energy to transport oil products, and the waste of [...] Read more.

One of the main problems in oil fields is the deposition of scale inside oil pipelines, which causes problems such as the reduction of the internal diameter of oil pipes, the need for more energy to transport oil products, and the waste of energy. For this purpose, the use of an accurate and reliable system for determining the amount of scale inside the pipes has always been one of the needs of the oil industry. In this research, a non-invasive, accurate, and reliable system is presented, which works based on the attenuation of gamma rays. A dual-energy gamma source (²⁴¹Am and ¹³³Ba radioisotopes), a sodium iodide detector, and a steel pipe are used in the structure of the detection system. The configuration of the detection structure is such that the dual-energy source and the detector are directly opposite each other and on both sides of the steel pipe. In the steel pipe, a stratified flow regime consisting of gas, water, and oil in different volume percentages was simulated using Monte Carlo N Particle (MCNP) code. Seven scale thicknesses between 0 and 3 cm were simulated inside the tube. After the end of the simulation process, the received signals were labeled and transferred to the frequency domain usage of fast Fourier transform (FFT). Frequency domain signals were processed, and four frequency characteristics were extracted from them. The multilayer perceptron (MLP) neural network was used to obtain the relationship between the extracted frequency characteristics and the scale thickness. Frequency characteristics were defined as inputs and scale thickness in cm as the output of the neural network. The prediction of scale thickness with an RMSE of 0.13 and the use of only one detector in the structure of the detection system are among the advantages of this research. Full article

(This article belongs to the Special Issue AI Technologies in Oil and Gas Geological Engineering)

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12 pages, 3311 KiB

Open AccessArticle

A Method for Evaluating the Rock Breaking Efficiency of Cutters and Optimizing the PDC Cutter Profile—A Study of Igneous Rock Formations in Shunbei Oilfield

by Zhuoxin Dong, Hui Zhang, Jun Li, Kuangsheng Zhang, Yangyong Ou, Zongyu Lu and Jiangang Shi

Energies 2022, 15(18), 6686; https://doi.org/10.3390/en15186686 - 13 Sep 2022

Cited by 4 | Viewed by 1491

Abstract

The Permian igneous rock in Shunbei Oilfield exhibits high rock strength, which results in a low rate of penetration (ROP) and shortens the cutter’s service life. It is necessary to analyze and evaluate the rock breaking effect of cutters. However, at this stage, [...] Read more.

The Permian igneous rock in Shunbei Oilfield exhibits high rock strength, which results in a low rate of penetration (ROP) and shortens the cutter’s service life. It is necessary to analyze and evaluate the rock breaking effect of cutters. However, at this stage, the evaluation of the rock breaking effect has been limited to comparing the sizes of the mechanical specific energy (MSE), and the change in the rock breaking efficiency caused by the difference in the shape of the cutters’ surface has not been considered. Therefore, through the establishment of numerical simulation models of a circular cutter, bevel cutter, axe cutter, wedge cutter, and triangular cutter, the evaluation of the rock breaking efficiency of special-shaped cutters was completed. The results show that the triangular cutter and the wedge cutter are suitable for the front row cutter of the polycrystalline diamond compact bit (PDC); the triangular cutter is suitable for drilling into medium–hard formations, the wedge cutter is suitable for drilling into hard formations, and the bevel cutter is suitable for the back row cutter of the PDC, to assist other cutters in the process of rock breaking. The research results can provide the basis for the selection of PDC bit cutters and the design optimization of the bit. Full article

(This article belongs to the Special Issue AI Technologies in Oil and Gas Geological Engineering)

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16 pages, 16780 KiB

Open AccessArticle

Quantitative Seismic Interpretation of Reservoir Parameters and Elastic Anisotropy Based on Rock Physics Model and Neural Network Framework in the Shale Oil Reservoir of the Qianjiang Formation, Jianghan Basin, China

by Zhiqi Guo, Tao Zhang, Cai Liu, Xiwu Liu and Yuwei Liu

Energies 2022, 15(15), 5615; https://doi.org/10.3390/en15155615 - 2 Aug 2022

Cited by 1 | Viewed by 1847

Abstract

Quantitative estimates of reservoir parameters and elastic anisotropy using seismic methods is essential for characterizing shale oil reservoirs. Rock physics models were established to quantify elastic anisotropy associated with clay properties, laminated microstructures, and bedding fractures at different scales in shale. The inversion [...] Read more.

Quantitative estimates of reservoir parameters and elastic anisotropy using seismic methods is essential for characterizing shale oil reservoirs. Rock physics models were established to quantify elastic anisotropy associated with clay properties, laminated microstructures, and bedding fractures at different scales in shale. The inversion schemes based on the built rock physics models were proposed to estimate reservoir parameters and elastic anisotropy using well log data. Based on the back propagation neural network framework, the obtained rock physical inversion results were used to establish the nonlinear models between elastic properties and reservoir parameters and elastic anisotropy of shale. The established correlations were applied for quantitative seismic interpretation, converting seismic inversion results to the reservoir parameters and elastic anisotropy to characterize the shale oil reservoir comprehensively. The predicted elastic anisotropy of the shale matrix reflects the lamination degree and the mechanical properties of the shale, which is critical for the effective implementation of hydraulic fracturing. The calculated elastic anisotropy of the shale provides more accurate models for seismic modeling and inversion. The obtained bedding fracture parameters provide insights into reservoir permeability. Therefore, the proposed method provides valuable information for identifying favorable oil zones in the study area. Full article

(This article belongs to the Special Issue AI Technologies in Oil and Gas Geological Engineering)

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12 pages, 1165 KiB

Open AccessArticle

The Analytical Model for the Impact Assessment of the Magnetic Treatment of Oil on the Wax Deposition Rate on the Tubing Wall

by Nikolay Cheremisin, Ivan Struchkov and Alexander Cheremisin

Energies 2022, 15(15), 5445; https://doi.org/10.3390/en15155445 - 27 Jul 2022

Cited by 3 | Viewed by 1245

Abstract

There has been a large amount of experience in recent decades in the use of magnetic fields on reservoir fluids. This paper discusses the effect of a magnetic field on wax precipitation. An analytical model is developed to quantify the wax deposition rate [...] Read more.

There has been a large amount of experience in recent decades in the use of magnetic fields on reservoir fluids. This paper discusses the effect of a magnetic field on wax precipitation. An analytical model is developed to quantify the wax deposition rate on the tubing surface during the magnetic treatment of reservoir oil. It has been established that the passage of the oil flow through a non-uniform magnetic field causes a high-intensity electric field for a sufficiently long period of time, the effect of which decreases the solubility of wax in oil, increases the intensity of wax precipitation in oil, and reduces the wax deposition on the tubing surface. The model accounts for the fact that the wax deposits present on the tubing surface are a highly efficient heat insulator that changes the temperature regime of the flow and the temperature of the tubing wall. This circumstance changes the rate of deposits but does not make these deposits less harmful to wells’ operation. A method for calculating the equilibrium wax concentration and changing the solubility of wax in oil under a constant electric field has been developed. We show that the effect of magnetic treatments on wax deposition rises with the increase in the concentration of asphaltenes in the oil and water cut. Full article

(This article belongs to the Special Issue AI Technologies in Oil and Gas Geological Engineering)

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15 pages, 3130 KiB

Open AccessArticle

Research on Prediction Method of Volcanic Rock Shear Wave Velocity Based on Improved Xu–White Model

by Hanqing Qiao, Bing Zhang and Cai Liu

Energies 2022, 15(10), 3611; https://doi.org/10.3390/en15103611 - 15 May 2022

Cited by 1 | Viewed by 1616

Abstract

Volcanic rock reservoirs have received extensive attention from scholars all over the world because of their geothermal, mineral, and oil and gas resources. Shear wave velocity is the essential information for AVO (amplitude variation with offset) analysis and the reservoir description of volcanic [...] Read more.

Volcanic rock reservoirs have received extensive attention from scholars all over the world because of their geothermal, mineral, and oil and gas resources. Shear wave velocity is the essential information for AVO (amplitude variation with offset) analysis and the reservoir description of volcanic rocks. However, due to factors such as cost, technical reasons, and so on, shear wave velocity is not provided in many logging data. This paper proposes a shear wave velocity prediction method suitable for the conventional logging of volcanic rocks. Firstly, the Xu–White model is improved. The probability distributions formed by the prior information of the logging area are used to initialize the key petrophysical parameters in the model instead of the fixed parameter value to establish the statistical petrophysical model between the logging curve and shear wave velocity. Then, based on the Bayesian inversion method, the simulated P-wave velocity is matched with the actual P-wave logging data to calculate the key petrophysical parameters, and is then used for S-wave velocity prediction. The method is applied to the actual logging data of the No. 5 structure in Nanpu Sag, eastern China. The prediction effect of shear wave velocity is better than that of the conventional method, indicating the feasibility and effectiveness of this method. This study will provide more accurate shear wave velocity data for the exploration and development of volcanic reservoirs. Full article

(This article belongs to the Special Issue AI Technologies in Oil and Gas Geological Engineering)

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