Hierarchical Surrogate-Assisted Evolutionary Algorithm for Integrated Multi-Objective Optimization of Well Placement and Hydraulic Fracture Parameters in Unconventional Shale Gas Reservoir

Zhou, Jun; Wang, Haitao; Xiao, Cong; Zhang, Shicheng

doi:10.3390/en16010303

Cited by 11 publications

(4 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In other words, GA optimization accuracy is typically challenging to sustain within a certain number of iterations. Zhou and Ran (2023) proposed a modified GA based on the Spearman correlative coefficient (SGA) to improve the optimization speed and accuracy. Compared to GA, SGA modifies the crossover and mutation rates' determination methods.…”

Section: Asga-xgboost Modelmentioning

confidence: 99%

Production prediction based on ASGA-XGBoost in shale gas reservoir

Zhou,

Ran

2023

Energy Exploration & Exploitation

View full text Add to dashboard Cite

The advancement of horizontal drilling and hydraulic fracturing technologies has led to an increased significance of shale gas as a vital energy source. In the realm of oilfield development decisions, production forecast analysis stands as an essential aspect. Despite numerical simulation being a prevalent method for production prediction, its time-consuming nature is ill-suited for expeditious decision-making in oilfield development. Consequently, we present a data-driven model, ASGA-XGBoost, designed for rapid and precise forecasting of shale gas production from horizontally fractured wells. The central premise of ASGA-XGBoost entails the implementation of ASGA to optimize the hyperparameters of the XGBoost model, thereby enhancing its prediction performance. To assess the feasibility of the ASGA-XGBoost model, we employed a dataset comprising 250 samples, acquired by simulating shale gas multistage fractured horizontal well development through the use of CMG commercial numerical simulation software. Furthermore, XGBoost, GA-XGBoost, and ASGA-XGBoost models were trained using the data from the training set and employed to predict the 30-day cumulative gas production utilizing the data from the testing set. The outcomes demonstrate that the ASGA-XGBoost model yields the lowest mean absolute error and offers optimal performance in predicting the 30-day cumulative gas production. Additionally, the mean absolute error of the unoptimized XGBoost model is markedly greater than that of the optimized XGBoost model, indicating that the latter, refined through the application of intelligent optimization algorithms, exhibits superior performance. The insights gleaned from this investigation have the potential to inform the development of strategic plans for shale gas oilfields, ultimately promoting the cost-effective exploitation of this energy resource.

show abstract

Section: Asga-xgboost Modelmentioning

confidence: 99%

Production prediction based on ASGA-XGBoost in shale gas reservoir

Zhou,

Ran

2023

Energy Exploration & Exploitation

View full text Add to dashboard Cite

show abstract

“…According to the differences in the pattern of MFHWs, there are mainly two types of well pattern for the Denglouku gas reservoir, which are parallel and zipper patterns. The zipper pattern can be divided into two further types, which are rows and columns of the zipper well pattern [41,42]. To compare the production performance of infilling wells for diverse well patterns, according to the geological characteristics and the techniques of drilling and completing for the Denglouku gas reservoir, a typical well group is selected, and the basic parameters of numerical simulation for the above-mentioned well pattern are determined (see Table 1).…”

Section: Optimization Of Infilling Well Typesmentioning

confidence: 99%

Enhanced Gas Recovery for Tight Gas Reservoirs with Multiple-Fractured Horizontal Wells in the Late Stages of Exploitation: A Case Study in Changling Gas Field

Ning,

Li,

Zhong

et al. 2023

Energies

View full text Add to dashboard Cite

To initially improve the gas production rate and shorten the payback period for tight gas reservoirs, the multiple-fractured horizontal well (MFHW) model is always applied. However, in the late stages of exploitation, it is difficult to adopt reasonable measures for enhanced gas recovery (EGR), particular for continental sedimentary formation with multiple layers, and efficient strategies for EGR in this type of gas field have not yet been presented. Therefore, in this paper, a typical tight gas reservoir in the late stages of exploitation, the Denglouku gas reservoir in Changling gas field, in which MFHWs were utilized and contributed to the communication of the higher Denglouku formation (0.34 mol% CO2) and lower Yingcheng formation (27 mol% CO2) during hydraulic fracturing, is studied comprehensively. Firstly, alongside the seismic, logging, drilling and experimental data, 3D geological and numerical simulation models are developed. According to the differences in CO2 mole fractions for different formations, the gas production rate of MFHWs produced from Denglouku formation is accurately calculated. Then, the well gas production rate (WGPR) and the well bottom-hole pressure (WBHP) history are matched with the calculated values, and thus the types of remaining gas are provided through the fine reservoir description. Finally, in a combination of gas recovery and economics, the optimal infill well type and the adjustment scheme are determined. The results show that there are three main categories of remaining gas, which are areal distribution, abundant points, and marginal dispersion, and the ratios of reaming gas reserve for these three types are 80.3%, 4.2%, and 15.5%, respectively. For the tight gas reservoir developed by MFHWs with parallel and zipper patterns, the best infilling well type is the vertical well. The combination of patching holes, sidetracking, infilling and boosting can obtain the highest gas recovery, while the scheme with patching holes and sidetracking has the best economic benefits. To balance the gas recovery and economics, the measurement of patching holes, sidetracking and infilling with vertical wells is utilized. In the final production period, compared with the basic schemes, the gas recovery can increase by 5.5%. The primary novelty of this paper lies in the determination of the optimal infilling well types and its presentation of a comprehensive adjustment workflow for EGR in tight gas reservoirs. The conclusions in this paper can provide some guidance for other similar tight gas reservoirs developed with MFHWs in the later period.

show abstract

“…Chen et al [71] proposed a surrogate assisted evolutionary algorithm with the dimensionality reduction method for water flooding production optimization, where the Sammon mapping is adopted as the dimension reduction method, and the Kriging model with lower confidence bound (LCB) is employed to estimate promising solutions. Zhou et al [72] developed a hierarchical surrogate-assisted evolutionary algorithm with multi-fidelity modeling technology for multi-objective shale gas reservoir problems, both the net present value and cumulative gas production are regarded as objective functions, where the low-fidelity model can adopt exploration behaviors, and the high-fidelity model can generate high-quality solutions as a local search operator. Tang et al [73] proposed an adaptive dynamic surrogate-assisted evolutionary algorithm for the aerodynamic shape design optimization of transonic airfoil and wing.…”

Section: Ensemble Surrogate Model Assisted Easmentioning

confidence: 99%

Cooperative coevolutionary surrogate ensemble-assisted differential evolution with efficient dual differential grouping for large-scale expensive optimization problems

Zhong,

Zhang,

Munetomo

2023

Complex Intell. Syst.

View full text Add to dashboard Cite

This paper proposes a novel algorithm named surrogate ensemble assisted differential evolution with efficient dual differential grouping (SEADECC-EDDG) to deal with large-scale expensive optimization problems (LSEOPs) based on the CC framework. In the decomposition phase, our proposed EDDG inherits the framework of efficient recursive differential grouping (ERDG) and embeds the multiplicative interaction identification technique of Dual DG (DDG), which can detect the additive and multiplicative interactions simultaneously without extra fitness evaluation consumption. Inspired by RDG2 and RDG3, we design the adaptive determination threshold and further decompose relatively large-scale sub-components to alleviate the curse of dimensionality. In the optimization phase, the SEADE is adopted as the basic optimizer, where the global and the local surrogate model are constructed by generalized regression neural network (GRNN) with all historical samples and Gaussian process regression (GPR) with recent samples. Expected improvement (EI) infill sampling criterion cooperated with random search is employed to search elite solutions in the surrogate model. To evaluate the performance of our proposal, we implement comprehensive experiments on CEC2013 benchmark functions compared with state-of-the-art decomposition techniques. Experimental and statistical results show that our proposed EDDG is competitive with these advanced decomposition techniques, and the introduction of SEADE can accelerate the convergence of optimization significantly.

show abstract

Hierarchical Surrogate-Assisted Evolutionary Algorithm for Integrated Multi-Objective Optimization of Well Placement and Hydraulic Fracture Parameters in Unconventional Shale Gas Reservoir

Cited by 11 publications

References 35 publications

Production prediction based on ASGA-XGBoost in shale gas reservoir

Production prediction based on ASGA-XGBoost in shale gas reservoir

Enhanced Gas Recovery for Tight Gas Reservoirs with Multiple-Fractured Horizontal Wells in the Late Stages of Exploitation: A Case Study in Changling Gas Field

Cooperative coevolutionary surrogate ensemble-assisted differential evolution with efficient dual differential grouping for large-scale expensive optimization problems

Contact Info

Product

Resources

About