Improved CRM Model for Inter-Well Connectivity Estimation and Production Optimization: Case Study for Karst Reservoirs

Wang, Daigang; Li, Yong; Zhang, Jing; Wei, Chenji; Jiao, Yue; Wang, Qi

doi:10.3390/en12050816

Cited by 16 publications

(7 citation statements)

References 39 publications

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“…The Koval model is used to address the characterization of viscous and heterogeneity effects using the so-called viscous fingering [115]. Wang et al [116] later implemented an improved CRM-Koval model coupled with aquifer support using the Karst reservoir as a case study. While the Koval model may be a good choice for production prediction from carbonate reservoirs, it is not sufficient to describe production from mature fields.…”

Section: Data-driven Optimization Approachmentioning

confidence: 99%

Untitled

2023

JESTR

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Waterflooding is the most common secondary recovery technique used in oil and gas industries today, owing to its cheap investment cost and easy implementation. However, major challenges are encountered in terms of oil sweep efficiency and breakthrough time which poses a risk to production and economic lifecycle of reservoirs. As a result, reservoir engineers are tasked with improvising optimal production strategies with the goal of maximizing profit. This review extensively describes some common optimization techniques reported in improving oil reservoir production. Also, their formulation, limitations and advantages with respect to production rates, oil well placement and control, inter-well connectivity and reservoir sweep efficiency were reviewed. While there are several optimization algorithms used in waterflooding, the emphasis in this work involves only the gradient and data driven optimizers since it is impossible to cover all optimization technique in a single review paper. Basically, no algorithm has been globally accepted as superior to the other since the sole aim is to improve productivity and economic profit, and each of these techniques has its unique practicability. However, when considering factors like design limitations, computational and economic cost, implementation timeframe, availability of data, some technique may suffice.

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Section: Data-driven Optimization Approachmentioning

confidence: 99%

Untitled

2023

JESTR

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“…Phương pháp xử lý tín hiệu phi tuyến được ứng dụng rộng rãi trong các lĩnh vực kỹ thuật. Một số các công trình nghiên cứu trên thế giới sử dụng phương pháp xử lý tín hiệu phi tuyến để đánh giá ảnh hưởng giữa giếng bơm ép và giếng khai thác, bước đầu thu được kết quả khả quan như mô hình INSIM (inter-well-numerical simulation model) [2], mô hình điện trở điện dung [3,4], phương pháp chỉ số khai thác đa giếng (MPI) [5]. Trong đó, mô…”

Section: Giới Thiệuunclassified

“…PETROVIETNAM hình điện trở điện dung sơ khai (CRM) [3] và sau này là mô hình điện trở điện dung cải tiến ICRMIP [4] với khả năng tính toán độc lập và đồng thời mức độ ảnh hưởng của tầng nước đáy và giếng bơm ép đến các giếng khai thác, khả năng dự báo đường cơ sở, khả năng dự báo đới thấm, đứt gãy giữa giếng bơm ép và giếng khai thác. Từ năm 2019, Viện Dầu khí Việt Nam (VPI) đã xây dựng phần mềm đánh giá ảnh hưởng của giếng bơm ép tới giếng khai thác dựa trên mô hình điện trở điện dung sơ khai.…”

Section: Tóm Tắtunclassified

Improving the quality of production simulation model by applying the results of nonlinear signal processing methods

Tran¹,

Tran²,

Pham³

et al. 2022

PVJ

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The petroleum production simulation model is a reliable and commonly used tool by petroleum engineers in field operation and management. Production history matching is a vital link while building and completing the simulation model such that it accurately reflects reservoir behavior. In addition to the methods such as direct modification and automatic/assisted history matching, the authors propose a solution to improve the efficiency of history matching by applying the results of nonlinear signal processing methods and data point mapping method through interpolation algorithm. The method is applied for 3 water injection wells, 10 production wells at Miocene reservoir of the water flooding field X in Cuu Long Basin. The results show that the water-cut parameter of 7/10 production wells really improved in comparison to the initial model. The error of oil and liquid cumulative production in the simulation model compared to reality data respectively decreases from -2.8% to -0.3% and from +11.7% to less than 5%.

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“…In the petroleum industry, the capacitance-resistance models (CRM) are widely used. The classical problem of the CRM model calibration is in the scope of the various classical [6] and recent papers [7,8]. However, it is required to choose between the plenty of the existing history-matching approaches to obtain the desired quality for the given site.…”

Section: Introductionmentioning

confidence: 99%

Hybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea

Nikitin,

Revin,

Hvatov

et al. 2021

Preprint

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The paper describes the usage of intelligent approaches for field development tasks that may assist a decision-making process. We focused on the problem of wells location optimization and two tasks within it: improving the quality of oil production estimation and estimation of reservoir characteristics for appropriate wells allocation and parametrization, using machine learning methods. For oil production estimation, we implemented and investigated the quality of forecasting models: physics-based, pure data-driven, and hybrid one. The CRMIP model was chosen as a physics-based approach. We compare it with the machine learning and hybrid methods in a frame of oil production forecasting task. In the investigation of reservoir characteristics for wells location choice, we automated the seismic analysis using evolutionary identification of convolutional neural network for the reservoir detection. The Volve oil field dataset was used as a case study to conduct the experiments. The implemented approaches can be used to analyze different oil fields or adapted to similar physics-related problems.

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Improved CRM Model for Inter-Well Connectivity Estimation and Production Optimization: Case Study for Karst Reservoirs

Cited by 16 publications

References 39 publications

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Improving the quality of production simulation model by applying the results of nonlinear signal processing methods

Hybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea

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