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Geomechanics and Reservoirs: Modeling and Simulation

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: closed (30 October 2023) | Viewed by 16965

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Prof. Dr. Haizhu Wang

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Guest Editor

State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China
Interests: fluid mechanics in petroleum engineering; high pressure water jet; well drilling and completion

Dr. Zhuang Sun

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Guest Editor

Dassault Systèmes, 343 Sansome St, San Francisco, CA 94104-5607, USA
Interests: micromechanics; reservoir geomechanics; CO₂ geological storage

Special Issue Information

Dear Colleagues,

We are inviting manuscript submissions for our upcoming Special Issue entitled Geomechanics and Reservoirs: Modeling and Simulation.

Geomechanics integrates rock mechanics, fluid mechanics, geophysics, and geology to determine the mechanical behavior of geological materials and applies from the microscale to the modeling of wellbores, reservoirs, fields and basins. It is critical to reduce risks and optimize rewards related to mechanical failure of the reservoir and surrounding formations.

This Special Issue aims to cover multidisciplinary studies and provide a cutting-edge look at original research in geomechanics modeling and numerical simulations of subsurface. Topics of interest include, but are not limited to, coupled thermal–hydrological–mechanical–chemical (THMC) modeling, multiscale characterization and modeling, constitutive behavior, micromechanics, time effects, artificial intelligence and digital twin in geomechanics, conventional and unconventional reservoirs, geothermal energy, gas hydrate, waste disposal, subsurface storage and sequestration, salt systems and induced seismicity.

Prof. Dr. Haizhu Wang
Dr. Zhuang Sun
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

numerical simulation
multiscale and multiphysics modeling
micromechanics
constitutive behavior
artificial intelligence and digital twin
conventional and unconventional reservoirs
geothermal
gas hydrate
subsurface storage

Published Papers (11 papers)

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Research

14 pages, 3425 KiB

Open AccessArticle

A Data-Driven Approach to Predict the ROP of Deep Wells in Fukang Sag

by Yingjie Wang, Qiang Tan, Desheng Wu, Hao Chen, Naikun Hu and Yuxuan Zhao

Appl. Sci. 2023, 13(22), 12471; https://doi.org/10.3390/app132212471 - 18 Nov 2023

Viewed by 588

Abstract

In the deep well drilling process in the Fukang Depression of the Eastern Junggar Basin, rock fracturing issues and low rate of penetration (ROP) have posed significant challenges to drilling efficiency. Accurate predictions of ROP prior to drilling are of considerable value in this context. Precise predictions enable on-site engineering teams to proactively identify drilling difficulties and anticipate potential complex scenarios, facilitating them in designing preventive measures in advance, such as selecting appropriate drill bits, adjusting drilling parameters, or employing specific drilling techniques to address these issues. This, in turn, enhances drilling efficiency and greatly reduces drilling risks. Traditional mechanical-specific energy drilling rate models, despite their widespread use, exhibit significant disparities with actual results when predicting ROP. These models only consider the influence of drill bits, drilling tools, and some drilling parameters on ROP, failing to adequately account for the variations caused by engineering factors and failing to capture the interrelationships between various parameters, especially when dealing with complex subsurface formations in the Fukang Depression. Random forest is a non-parametric algorithm in the field of machine learning that is suitable for analyzing and predicting ROP affected by various complex and non-linear drilling parameters. This paper establishes a Random forest model based on a dataset containing multiple variables of logging parameters and the actual ROP. The model ranks and assesses the important feature parameters of ROP to reveal their impact. Additionally, the model uses bootstrap sampling and feature random selection to construct multiple decision trees, reducing the risk of overfitting and endowing the model with a high generalization capability. Evaluation metrics indicate that the model exhibits a high prediction accuracy and performs well, significantly improving the accuracy of mechanical drilling rate predictions in the deep wells of the Fukang Depression. This model provides robust support and serves as a positive demonstration for addressing mechanical drilling rate issues in complex subsurface formations in the future. Full article

(This article belongs to the Special Issue Geomechanics and Reservoirs: Modeling and Simulation)

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22 pages, 6835 KiB

Open AccessArticle

Physically-Data Driven Approach for Predicting Formation Leakage Pressure: A Dual-Drive Method

by Huayang Li, Qiang Tan, Bojia Li, Yongcun Feng, Baohong Dong, Ke Yan, Jianqi Ding, Shuiliang Zhang, Jinlong Guo, Jingen Deng and Jiaao Chen

Appl. Sci. 2023, 13(18), 10147; https://doi.org/10.3390/app131810147 - 08 Sep 2023

Viewed by 813

Abstract

Formation leak-off pressure, which sets the upper limit of the safe drilling fluid density window, is crucial for preventing wellbore accidents and ensuring safe and efficient drilling operations. The paper thoroughly examines models of drilling physics alongside artificial intelligence techniques. The study introduces a dual-driven method for predicting reservoir pore pressure by integrating long short-term memory (LSTM) and backpropagation (BP) neural networks, where the core component is the LSTM-BP neural network model. The input data for the LSTM-BP model include wellbore diameter, formation density, sonic time, natural gamma, mud content, and pore pressure. The study demonstrates the practical application of the method using two vertical wells in Block M, employing the M-1 well for training and the M-2 well for validation. Two distinct input layer configurations are devised for the LSTM-BP model to evaluate the influence of formation density on prediction accuracy. Notably, Scheme 2 omits formation density as a variable in contrast to Scheme 1. The study’s results indicate that, for input layer configurations corresponding to Scenario 1 and Scenario 2, the LSTM-BP model exhibits relative error ranges of (−2.467%, 2.510%) and (−6.141%, 5.201%) on the test set, respectively. In Scenario 1, the model achieves mean squared error (MSE), mean absolute error (MAE), and R-squared (R²) values of 0.000229935, 0.011198329, and 0.92178272, respectively, on the test set. Conversely, for Scenario 2, the model demonstrates a substantial escalation of 992.393% and 240.674% in MSE and MAE, respectively, compared to Scenario 1; however, R² diminishes by 66.920%. Utilizing the trained LSTM-BP model, predictions for formation lost pressure in Well M-2 reveal linear correlation coefficients of 0.8173 and 0.6451 corresponding to Scenario 1 and Scenario 2, respectively. These findings imply that the predictions from the Scenario 1 model demonstrate stronger alignment with results derived from formulaic calculations. These observations remain consistent for both the BP neural network algorithm and the random forest algorithm. The aforementioned research results not only highlight the elevated predictive precision of the LSTM-BP model for intelligent prediction of formation lost pressure, a product of this study, thereby furnishing valuable data points to enhance the security of drilling operations in Block M, but also underscore the necessity of deliberating both physical relevance and data correlation during the selection of input layer variables. Full article

(This article belongs to the Special Issue Geomechanics and Reservoirs: Modeling and Simulation)

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19 pages, 5604 KiB

Open AccessArticle

A New Multi-Objective Optimization Design Method for Directional Well Trajectory Based on Multi-Factor Constraints

by Jianyu Qin, Luo Liu, Liang Xue, Xuyue Chen and Chengkai Weng

Appl. Sci. 2022, 12(21), 10722; https://doi.org/10.3390/app122110722 - 23 Oct 2022

Cited by 1 | Viewed by 1475

Abstract

The design of the wellbore trajectory directly affects the construction quality and efficiency of drilling. A good wellbore trajectory is conducive to guiding on-site construction, which can effectively reduce costs and increase productivity. Therefore, further optimization of the wellbore trajectory is inevitable and necessary. Based on this, aiming at the three-segment, five-segment, double-increase-profile extended reach wells, this paper considered the constraints of formation wellbore stability; formation strength; and the determination of the deviation angle, deviation point position, and target range by the work of deflecting tools. In addition, the optimization objective function of the shortest total length of the wellbore, minimum error of the second target, lowest cost, minimum friction of the lifting and lowering string, and minimum torque of rotary drilling were proposed and established. The objective function of the longest extension limit of the horizontal section of the extended reach well is established. Taking the 14-8 block of the Lufeng Oilfield in the eastern South China Sea as an example, the actual data of the field were modeled, and the independence of the objective function was verified by comparing the number of non-inferior solutions of the two objective functions. By normalizing simplified to double-, three-, and four-objective functions, using a genetic algorithm and particle swarm optimization results, it can be found that the new method of optimization design established in this paper has an obvious optimization effect compared with the original design. Full article

(This article belongs to the Special Issue Geomechanics and Reservoirs: Modeling and Simulation)

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7 pages, 4145 KiB

Open AccessCommunication

A New Mechanical Specific Energy Model for Composite Impact Drilling

by Jianyu Qin, Siyuan Yin, Naitong Yang, Xuyue Chen, Bo Tian, Liang Xue and Yi Ma

Appl. Sci. 2022, 12(20), 10356; https://doi.org/10.3390/app122010356 - 14 Oct 2022

Cited by 1 | Viewed by 1171

Abstract

Composite impact drilling technology is one of the important techniques to increase drilling speeds in deep hard formations. In order to evaluate the efficiency of drilling with a composite impactor in real time and effectively, and to further improve the drilling speed in deep formations, the mechanical specific energy (MSE) model of drilling with a composite impactor was studied. Based on previous theoretical studies on MSE, and considering the effect of the composite impactor on the axial and torsional impact of the drill bit during operation, a new MSE model for composite percussion drilling was established. The results show that the evaluation of the drilling efficiency by MSE obtained by this calculation model is consistent with the actual situation of a Lufeng X well. It can be more widely used in wells drilled with composite impact tools and meet the needs of field operations. Full article

(This article belongs to the Special Issue Geomechanics and Reservoirs: Modeling and Simulation)

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18 pages, 10147 KiB

Open AccessArticle

Evolution and Controlling Factors of the Reef and Carbonate Platform in Wan’an Basin, South China Sea

by Zhen Yang, Guangxue Zhang, Songfeng Liu, Xuebin Du, Lifei Wang, Wei Yan and Lei Huang

Appl. Sci. 2022, 12(18), 9322; https://doi.org/10.3390/app12189322 - 17 Sep 2022

Cited by 1 | Viewed by 1432

Abstract

In this study, high-resolution seismic profiles and well data provided a good opportunity for better understanding the reefs and carbonate platforms in the Wan’an Basin, southwest of the South China Sea, and also provided valuable information for the oil–gas exploration in the reef reservoirs. Four evolutional phases, including the initial phase, the prosperous phase, the recession phase and the submerged phase, of the reefs and carbonate platforms are proposed according to our data. In the Early Miocene, a few small, isolated carbonate platforms initiated in the center of the basin. In the Middle Miocene, they flourished and mainly formed around the Northern Uplift and Central Uplift, with two belts of carbonate platforms in the western area and eastern area that were mainly platform-edge reefs, massive reefs and a few point reefs. In the Late Miocene, the carbonate platforms began to retreat towards the high topographic position because of the rising of sea level. Meanwhile, the numbers and styles of reefs increased to include platform-edge reefs, massive reefs, atoll reefs and point reefs. Since the Pliocene, most of the carbonate platforms have been covered by detrital materials from terrestrial sources. Crustal tectonic activity provides favorable topography for reef growth and the distribution of platforms, and eustasy controlled the vertical growth and lateral migration of reefs. Since the Late Miocene, the rapidly crustal tectonic subsidence and the rising of relative sea level may lead to the drowning of the carbonate platform. Full article

(This article belongs to the Special Issue Geomechanics and Reservoirs: Modeling and Simulation)

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20 pages, 8289 KiB

Open AccessArticle

Propagation Characteristics of Fractures Induced by Supercritical Carbon Dioxide Jet in Hard and Soft Layered Rocks

by Feng Liu, Yi Hu and Jiawei Liu

Appl. Sci. 2022, 12(18), 9013; https://doi.org/10.3390/app12189013 - 08 Sep 2022

Viewed by 1245

Abstract

The initiation and propagation behavior of fractures induced by supercritical carbon dioxide (SC-CO₂) jet fracturing is significant to evaluate stimulated reservoir volume (SRV). However, the propagation characteristics of fractures induced by SC-CO₂ jet in layered rocks with layers having different mechanical properties have not yet been studied. In this study, four groups of artificial sandstones were used to conduct SC-CO₂ jet fracturing experiments and investigate the fracture initiation and propagation behavior in hard and soft layered rocks. A strain collection device was employed to monitor the strain changes of the specimens during the experiments, and after the experiments, a three-dimensional scanner was used to obtain the morphologies of the main fractures. Experimental results showed that the SC-CO₂ jet fracturing can be divided into the pressurization of the perforation pressure stage and fracture propagation stage, and the fractures initiation and propagation is intermittent. Three types of main fractures have been found—longitudinal fracture, transverse fracture and oblique fracture—and the formation mechanism of the main fractures has been elaborated. The rock strength can affect the number and complexity of fractures created and the fracturing rate, and the Young’s modulus of rock has an effect on fracture propagation length. The fractures mainly develop near the perforation and are difficult to propagate to another layer with different mechanical properties. The result in our study is conducive to the application of SC-CO₂ jet fracturing technology in the field. Full article

(This article belongs to the Special Issue Geomechanics and Reservoirs: Modeling and Simulation)

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23 pages, 19352 KiB

Open AccessArticle

The Propagation of Hydraulic Fractures in a Natural Fracture Network: A Numerical Study and Its Implications

by Yiwei Liu, Yi Hu and Yong Kang

Appl. Sci. 2022, 12(9), 4738; https://doi.org/10.3390/app12094738 - 08 May 2022

Cited by 3 | Viewed by 1523

Abstract

Natural fractures play a significant role in creating a fracture network simulation treatment. In this work, global cohesive elements were incorporated into the cohesive zone method to realize the unprompted propagation of a hydraulic fracture. The step-by-step propagation patterns of hydraulic fractures in a random natural fracture network were discussed. An effective area was defined to quantitively assess the influenced area of induced fractures. The results showed that the hydraulic fracture tips were attracted by local natural fractures when the horizontal stress difference was low. Bifurcations and secondary fractures occurred at the natural fracture intersections, which contributed to the complexity of the induced fracture network on a local scale. The length of the main hydraulic fracture reached the maximum when the in situ stress ratio was 1.12. The influence of natural fractures on the overall trend of fracture propagation was limited when the in situ stress difference increased. It also suggested that a lower rock tensile strength and natural fractures cementation strength improved the main fracture length. A higher tensile strength of rock increased the initiation pressure of the induced fracture, while the cementing strength of the natural fractures showed no impact on it. The results presented in this paper could improve the basic understanding of the fracture development in a natural network and help to predict a complex fracture network in a real situation. Full article

(This article belongs to the Special Issue Geomechanics and Reservoirs: Modeling and Simulation)

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16 pages, 5840 KiB

Open AccessArticle

Mechanical Analysis of DS in Horizontal Directional Drilling

by Hua Tong and Yongbo Shao

Appl. Sci. 2022, 12(6), 3145; https://doi.org/10.3390/app12063145 - 19 Mar 2022

Cited by 3 | Viewed by 2202

Abstract

In recent years, trenchless horizontal directional drilling (HDD) technology has developed rapidly towards the direction of large pipe diameter and long span. During construction, the buckling deformation and large drag torque of the drillstring (DS) are the key problems restricting the development of the HDD technology. In order to understand the buckling deformation law of the DS in horizontal directional drilling engineering, as well as the transfer and the distribution of the drag torque, and the relationship between the buckling deformation and drag torque, the dynamic model of the DS system in drilling engineering is applied to HDD engineering. The dynamics model of HDD at a long distance (5200 m) is established and calculated. The research indicates (1) when the buckling deformation of the DS is small, priority should be given to reducing the friction and torsion at the bending position; (2) excessive inlet thrust is the main reason for the buckling deformation of the DS, and the smaller the hole diameter, the more serious the buckling deformation; (3) severe buckling deformation of the DS will lead to an increase in the frictional resistance at the deformed part, and even local self-locking, resulting in the inability to continue to increase the weight on bit (WOB). The research results preliminarily reveal the relationship between DS buckling deformation and drag torque in ultra-long distance HDD engineering and point out the phenomenon law of drag torque and buckling deformation, which has reference value for the development of HDD technology. Full article

(This article belongs to the Special Issue Geomechanics and Reservoirs: Modeling and Simulation)

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28 pages, 9519 KiB

Open AccessFeature PaperArticle

Numerical Parametric Studies on the Stress Distribution in Rocks around Underground Silo

by Sun-Hoon Kim and Kwang-Jin Kim

Appl. Sci. 2022, 12(3), 1613; https://doi.org/10.3390/app12031613 - 03 Feb 2022

Cited by 3 | Viewed by 1346

Abstract

The underground silo was constructed as a facility for the disposal of low- and intermediate-level radioactive waste. It is divided into three parts: the upper-dome core, the lower-dome core, and the cylindrical-space core. Numerical parametric studies on the stress distribution occurring in the surrounding rocks around the underground silo are presented in this paper. It is assumed that the soil layer was distributed to a depth of −4.3 m from the ground level, the weathered rocks were distributed to a depth of −9.5 m from the bottom of the soil layer, and the rocks were distributed in the lower part of the weathered rocks. A 2D axial symmetric finite element model was considered for the numerical analysis of the underground silo. A 3D finite element model was used to verify the reliability of the 2D axial symmetric model. Finite element analysis was carried out under various ratios of in situ horizontal stress to vertical stress (Ko). The numerical results obtained through these analyses include detailed stress states in the p–q and octahedral planes at key locations of finite element models around an underground silo. Contours of safety factor distributions are also presented to evaluate the overall structural safety of the surrounding rock mass, which is the main supporting body of the underground silo. Full article

(This article belongs to the Special Issue Geomechanics and Reservoirs: Modeling and Simulation)

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11 pages, 3137 KiB

Open AccessArticle

Productivity Prediction of Fractured Horizontal Well in Shale Gas Reservoirs with Machine Learning Algorithms

by Tianyu Wang, Qisheng Wang, Jing Shi, Wenhong Zhang, Wenxi Ren, Haizhu Wang and Shouceng Tian

Appl. Sci. 2021, 11(24), 12064; https://doi.org/10.3390/app112412064 - 17 Dec 2021

Cited by 10 | Viewed by 2387

Abstract

Predicting shale gas production under different geological and fracturing conditions in the fractured shale gas reservoirs is the foundation of optimizing the fracturing parameters, which is crucial to effectively exploit shale gas. We present a multi-layer perceptron (MLP) network and a long short-term memory (LSTM) network to predict shale gas production, both of which can quickly and accurately forecast gas production. The prediction performances of the networks are comprehensively evaluated and compared. The results show that the MLP network can predict shale gas production by geological and fracturing reservoir parameters. The average relative error of the MLP neural network is 2.85%, and the maximum relative error is 12.9%, which can meet the demand of engineering shale gas productivity prediction. The LSTM network can predict shale gas production through historical production under the constraints of geological and fracturing reservoir parameters. The average relative error of the LSTM neural network is 0.68%, and the maximum relative error is 3.08%, which can reliably predict shale gas production. There is a slight deviation between the predicted results of the MLP model and the true values in the first 10 days. This is because the daily production decreases rapidly during the early production stage, and the production data change greatly. The largest relative errors of LSTM in this work on the 10th, 100th, and 1000th day are 0.95%, 0.73%, and 1.85%, respectively, which are far lower than the relative errors of the MLP predictions. The research results can provide a fast and effective mean for shale gas productivity prediction. Full article

(This article belongs to the Special Issue Geomechanics and Reservoirs: Modeling and Simulation)

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19 pages, 27993 KiB

Open AccessArticle

Prestack Seismic Inversion via Nonconvex L_1-2 Regularization

by Wenliang Nie, Fei Xiang, Bo Li, Xiaotao Wen and Xiangfei Nie

Appl. Sci. 2021, 11(24), 12015; https://doi.org/10.3390/app112412015 - 17 Dec 2021

Cited by 1 | Viewed by 1468

Abstract

Using seismic data, logging information, geological interpretation data, and petrophysical data, it is possible to estimate the stratigraphic texture and elastic parameters of a study area via a seismic inversion. As such, a seismic inversion is an indispensable tool in the field of oil and gas exploration and development. However, due to unknown natural factors, seismic inversions are often ill-conditioned problems. One way to work around this unknowable information is to determine the solution to the seismic inversion using regularization methods after adding further a priori constraints. In this study, the nonconvex L

_{1 - 2}

regularization method is innovatively applied to the three-parameter prestack amplitude variation angle (AVA) inversion. A forward model is first derived based on the Fatti approximate formula and then low-frequency models for P impedance, S impedance, and density are established using logging and horizon data. In the Bayesian inversion framework, we derive the objective function of the prestack AVA inversion. To further improve the accuracy and stability of the inversion results, we remove the correlations between the elastic parameters that act as initial constraints in the inversion. Then, the objective function is solved by the nonconvex L

_{1 - 2}

regularization method. Finally, we validate our inversion method by applying it to synthetic and observational data sets. The results show that our nonconvex L

_{1 - 2}

regularization seismic inversion method yields results that are highly accurate, laterally continuous, and can be used to identify and locate reservoir formation boundaries. Overall, our method will be a useful tool in future work focused on predicting the location of reservoirs. Full article

(This article belongs to the Special Issue Geomechanics and Reservoirs: Modeling and Simulation)

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