A new method for dynamic predicting porosity and permeability of low permeability and tight reservoir under effective overburden pressure based on BP neural network

Jiang, Daqing; Chen, Hao; Xing, Jianpeng; Wang, Yu; Wang, Zhilin; Tuo, Hong

doi:10.1016/j.geoen.2023.211721

Cited by 9 publications

(2 citation statements)

References 37 publications

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“…Zhang [66], Keramea [67] 2021-2023 Prediction of reservoir production and dynamic risk Ahmad [68,69], dos Santos [70], Tan [71], Yin [72] 2020-2022 Discharge and utilization of petroleum wastewater Guzman [73], He [74] 2021-2023 Detection of oil quality Mamudu [75], Yang [76] 2022-2023 Fault monitoring of oil production equipment Subasi [77], de Moura [78], Zhao [79], Jiang [80] 2020-2023 Prediction of oil reservoir permeability However, efficiency is crucial while striving to simplify processes and reduce operational costs. Pursuing higher efficiency can sometimes conflict with achieving the required production quality.…”

Section: Review Of Dynamic Problems In Complex Industrial Processesmentioning

confidence: 99%

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

Tian,

Zhao,

Xie

et al. 2024

Processes

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As industrial practices continue to evolve, complex process industries often exhibit characteristics such as multivariate correlation, dynamism, and nonlinearity, making traditional mechanism modeling inadequate in terms of addressing the intricacies of complex industrial problems. In recent years, with advancements in control theory and industrial practices, there has been a substantial increase in the volume of industrial data. Data-driven dynamic operation optimization techniques have emerged as effective solutions for handling complex industrial processes. By responding to dynamic environmental changes and utilizing advanced optimization algorithms, it is possible to achieve dynamic operational optimization in industrial processes, thereby reducing costs and emissions, improving efficiency, and increasing productivity. This correlates nicely with the goals set forth by conventional process operation optimization theories. Nowadays, this dynamic, data-driven strategy has shown significant potential in complex process industries characterized by multivariate correlations and nonlinear behavior. This paper approaches the subject from a data-driven perspective by establishing dynamic optimization models for complex industries and reviewing the state-of-the-art time series forecasting models to cope with changing objective functions over time. Meanwhile, aiming at the problem of concept drift in time series, this paper summarizes new concept drift detection methods and introduces model update methods to solve this challenge. In addressing the problem of solving dynamic multi-objective optimization problems, the paper reviews recent developments in dynamic change detection and response methods while summarizing commonly used as well as the latest performance measures for dynamic multi-objective optimization problems. In conclusion, a discussion of the research progress and challenges in the relevant domains is undertaken, followed by the proposal of potential directions for future research. This review will help to deeply understand the importance and application prospects of data-driven dynamic operation optimization in complex industrial fields.

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Section: Review Of Dynamic Problems In Complex Industrial Processesmentioning

confidence: 99%

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

Tian,

Zhao,

Xie

et al. 2024

Processes

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show abstract

“…Various types of reservoir-permeability modes coexist. Due to the independent genesis and limited development space of different reservoir-permeability modes, most reservoir-permeability modes are distributed independently [25]. The considerable diversity of reservoir space in shell limestone led to the use of the reservoirpermeability model showing continuous distribution characteristics.…”

Section: Continuous Distribution Of Reservoir Permeability Modelmentioning

confidence: 99%

Multi-Porous Medium Characterization Reveals Tight Oil Potential in the Shell Limestone Reservoir of the Sichuan Basin

Zhou,

Guo,

et al. 2024

Processes

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With the continuous deepening of oil and gas exploration and development, unconventional oil and gas resources, represented by tight oil, have become research hotspots. However, few studies have investigated tight oil potential in any systematic way in the shell limestone reservoir of the Sichuan Basin. Herein, we used thin section analysis, X-ray diffraction (XRD), high-pressure mercury intrusion, low-pressure N2 and CO2 adsorption experiments, low-field nuclear magnetic resonance (NMR), focused ion beam–scanning electron microscopy (FIB-SEM), and nano-CT to characterize multi-porous media. The reservoir space controlled by nonfabric, shell, and matrix constitutes all the reservoir space for tight oil. The interconnected porosity was mainly distributed in the range of 1% to 5% (avg. 2.12%). The effective interconnected porosity mainly ranged from 0.5% to 2.0% (avg. 1.59%). The porosity of large fractures was 0.1% to 0.5% (avg. 0.21%). The porosity of isolated pores and bound oil–water pores was 0.2% to 0.8% (avg. 0.44%). The dissolved pores adjacent to fractures, the microfractures controlled by the shell, the microfractures controlled by the matrix, the isolated pores, and the intracrystalline pores constitute five independent pore-throat systems. The development of pores and fractures in shell limestone reservoirs are coupled on the centimeter–millimeter–micron–nanometer scale. Various reservoir-permeability models show continuous distribution characteristics. These findings make an important contribution to the exploration and exploitation of tight oil in shell limestone.

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A novel method for predicting shallow hydrocarbon accumulation based on source-fault-sand (S-F-Sd) evaluation and ensemble neural network (ENN)

Wang,

Chen,

et al. 2024

Applied Energy

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A new method for dynamic predicting porosity and permeability of low permeability and tight reservoir under effective overburden pressure based on BP neural network

Cited by 9 publications

References 37 publications

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

Multi-Porous Medium Characterization Reveals Tight Oil Potential in the Shell Limestone Reservoir of the Sichuan Basin

A novel method for predicting shallow hydrocarbon accumulation based on source-fault-sand (S-F-Sd) evaluation and ensemble neural network (ENN)

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