Facies-Controlled Geostatistical Porosity Model for Estimation of the Groundwater Potential Area in Hongliu Coalmine, Ordos Basin, China

Li, Liyao; Wei, Jiuchuan; Xia, Fei; Gao, Jingqun; Liu, Chao

doi:10.1021/acsomega.0c06166

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…Compared to the 2D groundwater potential method used in previous research, 3D-visualized groundwater potential prediction can more directly depict the spatial form of aquifers. 3D visualized prediction can also quantitatively represent the groundwater potential of aquifers and connectivity between different aquifers in 3D space. , The 3D groundwater potential model can be used to slice profiles and divide arbitrary sections into different layers and directions, which is beneficial for mine hydrogeologists to prevent and predict mine water disasters. Sedimentary geological bodies were deposited in different geological periods; thus, the distributed rule of sand bodies in each period differs due to provenance supply and other reasons, leading to the complex heterogeneity of sedimentary strata.…”

Section: Result: a Case Studymentioning

confidence: 99%

3D Quantitative Prediction of the Groundwater Potential Area─A Case Study of a Simple Geological Structure Aquifer

Xia

Liu

et al. 2022

ACS Omega

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The Ordos Basin is a sedimentary basin located in Inner Mongolia, China, where coal and uranium coexist. Water inrush disasters have always been one of the main disasters that threaten the safety of coal mine production, and thus, the study and division of groundwater potential regions are of great significance for the prevention of water inrush disasters and in situ leaching of sandstone-type uranium ore. A new method combining truncated Gaussian simulation and sedimentary facies control was established to predict the groundwater potential area. Taking a typical aquifer, the Zhiluo Formation, as an example, based on high-resolution sequence stratigraphy, geophysics, sedimentary geology, and geostatistical theory, the plane distribution of sand bodies was predicted. Furthermore, the relationship between rock porosity and electricity porosity was established to calculate the regional porosity. Combined with truncated Gaussian simulation and facies-controlled modeling methods, a facies-controlled heterogeneous property model was established to analyze the heterogeneous effective porosity of the aquifer in the study area. Groundwater potential areas were quantitatively evaluated by 3D modeling analysis. The results of the evaluated model were verified by actual data and provide a geological guarantee for the accurate mining of deep coal and uranium ore. A 3D distributed model of chemical elements, which is meaningful for in situ leaching uranium mining, is expected in future research.

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Section: Result: a Case Studymentioning

confidence: 99%

3D Quantitative Prediction of the Groundwater Potential Area─A Case Study of a Simple Geological Structure Aquifer

Xia

Liu

et al. 2022

ACS Omega

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“…In the future, the hydrochemical characteristics of groundwater should also be taken into account in the selection of evaluation indexes. 63 − 65 …”

Section: Further Studymentioning

confidence: 99%

“…The hydrochemical influence of the aquifer cannot be ignored in the evaluation of water inrush risk. In the future, the hydrochemical characteristics of groundwater should also be taken into account in the selection of evaluation indexes. − …”

Section: Further Studymentioning

confidence: 99%

A Multifactor Quantitative Assessment Model for Safe Mining after Roof Drainage in the Liangshuijing Coal Mine

et al. 2022

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To prevent coal mine roof water damage, the water generally needs to be evacuated in advance. It can be mined with the water inrush risk assessed as safe. However, a single index is often employed in the water safety evaluation after the roof drainage, which causes a large gap between the evaluation results and the actual situation. Therefore, the evaluation cannot be effectively used to guide the safety mining in the working face. In this paper, based on the hydrogeological data of the Liangshuijing coal mine, a multifactor water inrush risk assessment model (IAHP-EWM) and multifactor index system are established for assessing the water inrush risk before and after the roof drainage. The improved AHP method and the entropy weight method are adopted in the model to determine the index weight. This combined way avoids the excessive subjectivity and objectivity of the index weight. A″ Fold undulation degree ( F ud )″ is innovatively proposed to quantify the impact of the spatial relief of folds on water inrush in the multifactor index system. The IAHP-EWM model is applied to evaluate the risk of roof water inrush in the 42205 working face of the Liangshuijing coal mine. The evaluation results show that the water inrush risk is ″high″ when the water is not dredged, and the water inrush risk is ″low″ after the water is dredged, which are consistent with the actual water inflow data and evaluation results, which verifies the accuracy of the model. The application results of the IAHP-EWM model in the 42202, 42203, and 42204 working faces verify its universal applicability in the Liangshuijing mining area. It can provide a reference for the evaluation of the roof water damage control effect during coal seam mining.

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Application of Reinforcement Learning for Well Location Optimization

Dawar

Srinivasan

Webster

2023

Springer Proceedings in Earth and Environmental Sciences

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The extensive deployment of sensors in oilfield operation and management has led to the collection of vast amounts of data, which in turn has enabled the use of machine learning models to improve decision-making. One of the prime applications of data-based decision-making is the identification of optimum well locations for hydrocarbon recovery. This task is made difficult by the relative lack of high-fidelity data regarding the subsurface to develop precise models in support of decision-making. Each well placement decision not only affects eventual recovery but also the decisions affecting future wells. Hence, there exists a tradeoff between recovery maximization and information gain. Existing methodologies for placement of wells during the early phases of reservoir development fail to take an abiding view of maximizing reservoir profitability, instead focusing on short-term gains. While improvements in drilling technologies have dramatically lowered the costs of producing hydrocarbon from prospects and resulted in very efficient drilling operations, these advancements have led to sub-optimal and haphazard placement of wells. This can lead to considerable number of unprofitable wells being drilled which, during periods of low oil and gas prices, can be detrimental for a company’s solvency. The goal of the research is to present a methodology that builds machine learning models, integrating geostatistics and reservoir flow dynamics, to determine optimum future well locations for maximizing reservoir recovery. A deep reinforcement learning (DRL) framework has been proposed to address the issue of long-horizon decision-making. The DRL reservoir agent employs intelligent sampling and utilizes a reward framework that is based on geostatistical and flow simulations. The implemented approach provides opportunities to insert expert information while basing well placement decisions on data collected from seismic data and prior well tests. Effects of prior information on the well placement decisions are explored and the developed DRL derived policies are compared to single-stage optimization methods for reservoir development. Under similar reward framework, sequential well placement strategies developed using DRL have been shown to perform better than simultaneous drilling of several wells.

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Facies-Controlled Geostatistical Porosity Model for Estimation of the Groundwater Potential Area in Hongliu Coalmine, Ordos Basin, China

Cited by 5 publications

References 26 publications

3D Quantitative Prediction of the Groundwater Potential Area─A Case Study of a Simple Geological Structure Aquifer

3D Quantitative Prediction of the Groundwater Potential Area─A Case Study of a Simple Geological Structure Aquifer

A Multifactor Quantitative Assessment Model for Safe Mining after Roof Drainage in the Liangshuijing Coal Mine

Application of Reinforcement Learning for Well Location Optimization

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