A transient two-phase flow model for production prediction of tight gas wells with fracturing fluid-induced formation damage

Wu, Yue; Cheng, Linsong; Ma, Liqiang; Huang, Shijun; Fang, Sidong; Killough, John; Jia, Peng; Wang, Suran

doi:10.1016/j.petrol.2021.108351

Cited by 62 publications

(38 citation statements)

References 44 publications

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“…Zhao and Du [16] proposed a new numerical model for the multistage fractured horizontal well to predict well production in tight oil reservoirs and concluded that their model can easily obtain eighth flow regimes. Wu et al [17] established a transient two-phase flow model to predict the oil production from fractured tight gas reservoirs with fluid-induced formation damage and concluded that their model can deal with production data in the flow back and production stage and evaluate the formation damage for unconventional reservoirs. Although much progress has been made in conventional numerical models on well production performance, the complexity of calculation is still an obstacle we cannot step over.…”

Section: Introductionmentioning

confidence: 99%

Well Performance from Numerical Methods to Machine Learning Approach: Applications in Multiple Fractured Shale Reservoirs

Liu

Kim

et al. 2021

Geofluids

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Horizontal well fracturing technology is widely used in unconventional reservoirs such as tight or shale oil and gas reservoirs. Meanwhile, the potential of enhanced oil recovery (EOR) methods including huff-n-puff miscible gas injection are used to further increase oil recovery in unconventional reservoirs. The complexities of hydraulic fracture properties and multiphase flow make it difficult and time-consuming to understand the well performance (i.e., well production) in fractured shale reservoirs, especially when using conventional numerical methods. Therefore, in this paper, two methods are developed to bridge this gap by using the machine learning technique to forecast well production performance in unconventional reservoirs, especially on the EOR pilot projects. The first method is the artificial neural network, through which we can analyze the big data from unconventional reservoirs to understand the underlying patterns and relationships. A bunch of factors is contained such as hydraulic fracture parameters, well completion, and production data. Then, feature selection is performed to determine the key factors. Finally, the artificial neural network is used to determine the relationship between key factors and well production performance. The second is time series analysis. Since the properties of the unconventional reservoir are the function of time such as fluid properties and reservoir pressure, it is quite suitable to apply the time series analysis to understand the well production performance. Training and test data are from over 10000 wells in different fractured shale reservoirs, including Bakken, Eagle Ford, and Barnett. The results demonstrate that there is a good match between the available and predicated well performance data. The overall R values of the artificial neural network and time series analysis are both above 0.8, indicating that both methods can provide reliable results for the prediction of well performance in fractured shale reservoirs. Especially, when dealing with the EOR field cases, such as huff-n-puff miscible gas injection, Time series analysis can provide more accurate results than the artificial neural network. This paper presents a thorough analysis of the feasibility of machine learning in multiple fractured shale reservoirs. Instead of using the time-consuming numerical methods, it also provides a more robust way and meaningful reference for the evaluation of the well performance.

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Section: Introductionmentioning

confidence: 99%

Well Performance from Numerical Methods to Machine Learning Approach: Applications in Multiple Fractured Shale Reservoirs

Liu

Kim

et al. 2021

Geofluids

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“…And the gas-transport mechanism in the coal/shale matrix cannot be simply described by Knudsen diffusion. However, popular-utilized numerical simulation and recently proposed production prediction models for shale gas reservoirs employ Knudsen diffusion to represent the gas-transport capacity in the shale matrix (Cipolla et al 2013;Wu et al 2021;Zhao et al 2020;Huang et al 2021;Dejam 2019;Gale et al 2007), which inevitably result in obvious deviation comparing the actual production behavior. As a result, it is crucial for the production prediction model or next-generation numerical simulator to account for the multiple gas-transport mechanism.…”

Section: Introductionmentioning

confidence: 99%

A non-empirical model for gas transfer through circular nanopores in unconventional gas reservoirs

Wei

Ji³

et al. 2021

J Petrol Explor Prod Technol

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It is difficult to predict the flow performance in the nanopore networks since traditional assumptions of Navier–Stokes equation break down. At present, lots of attempts have been employed to address the proposition. In this work, the advantages and disadvantages of previous analytical models are seriously analyzed. The first type is modifying a mature equation which is proposed for a specified flow regime and adapted to wider application scope. Thus, the first-type models inevitably require empirical coefficients. The second type is weight superposition based on two different flow mechanisms, which is considered as the reasonable establishment method for universal non-empirical gas-transport model. Subsequently, in terms of slip flow and Knudsen diffusion, the novel gas-transport model is established in this work. Notably, the weight factors of slip flow and Knudsen diffusion are determined through Wu’s model and Knudsen’s model respectively, with the capacity to capture key transport mechanism through nanopores. Capturing gas flow physics at nanoscale allows the proposed model free of any empirical coefficients, which is also the main distinction between our work and previous research. Reliability of proposed model is verified by published molecular simulation results as well. Furthermore, a novel permeability model for coal/shale matrix is developed based on the non-empirical gas-transport model. Results show that (a) nanoconfined gas-transport capacity will be strengthened with the decline of pressure and the decrease in the pressure is supportive for the increasing amplitude; (b) the greater pore size the nanopores is, the stronger the transport capacity the nanotube is; (c) after field application with an actual well in Fuling shale gas field, China, it is demonstrated that numerical simulation coupled with the proposed permeability model can achieve better historical match with the actual production performance. The investigation will contribute to the understanding of nanoconfined gas flow behavior and lay the theoretical foundation for next-generation numerical simulation of unconventional gas reservoirs.

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“…and layer structures inside the rock materials, the composite thin coal seam presents remarkably heterogeneous and discontinuous characteristics [20][21][22]. Besides, the generation of microcrack zone ahead of the fracture tip is the prominent fracturing characteristic of rock-like materials [23][24][25][26], and the seepage characteristics of injection-fluid in the HF microcrack-band aggravate the complexity of HF propagation [27,28]. Therefore, the traditional hydraulic fracturing models are supposed to be insufficient to delineate HF extension and permeability evolution in the composite thin coal seam.…”

Section: Introductionmentioning

confidence: 99%

Hydraulic Fracture Propagation and Permeability Evolution in the Composite Thin Coal Seam

Liu

Huang

et al. 2021

Geofluids

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Hydraulic fracturing can improve the permeability of composite thin coal seam. Recently, characterizing hydraulic fracture (HF) propagation inside the coal seam and evaluating the permeability enhancement with HF extension remain challenging and crucial. In this work, based on the geological characteristics of the coal seam in a coal mine of the southwest China, the RFPA2D-Flow software is employed to simulate the HF propagation and its permeability-increasing effect in the composite thin coal seam, and a couple of outcomes were obtained. (1) Continuous propagation of the hydraulic microcrack-band is the prominent characteristic of HF propagation. With the increment of the injection-water pressure, HF generation in the composite thin coal seam can be divided into three stages: stress accumulation, stable fracture propagation, and unstable fracture propagation. (2) The hydraulic microcrack-band propagates continuously driven by the fluid-injection pressure. The microcrack-band not only cracks the coal seam but also fractures the gangue sandwiched between the coal seams. (3) The permeability in the composite thin coal seam increases significantly with the propagation of hydraulic microcrack-band. The permeability increases by 1~2 magnitudes after hydraulic fracturing. This study provides references to the field applications of hydraulic fracturing in the composite thin coal seam, such as optimizing hydraulic fracturing parameters, improving gas drainage, and safe-efficient mining.

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A transient two-phase flow model for production prediction of tight gas wells with fracturing fluid-induced formation damage

Cited by 62 publications

References 44 publications

Well Performance from Numerical Methods to Machine Learning Approach: Applications in Multiple Fractured Shale Reservoirs

Well Performance from Numerical Methods to Machine Learning Approach: Applications in Multiple Fractured Shale Reservoirs

A non-empirical model for gas transfer through circular nanopores in unconventional gas reservoirs

Hydraulic Fracture Propagation and Permeability Evolution in the Composite Thin Coal Seam

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