A novel data-driven pressure/rate deconvolution algorithm to enhance production data analysis in unconventional reservoirs

Pan, Yuewei; Deng, Lichi; Lee, W. John

doi:10.1016/j.petrol.2020.107332

Cited by 16 publications

(4 citation statements)

References 12 publications

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“…1. Regression -prediction of a specific number based on an array of features or characteristics (Palmer 2009;Emeke 2019;S. Tou, 1988) ).…”

Section: Methodology and Materialsmentioning

confidence: 99%

Development of Oil Fields Using Science Artificial Intelligence and Machine Learning

Al-Obaidi,

Chang,

Hofmann

2023

nat

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Since artificial intelligence has become increasingly prevalent in the oil industry, it is relevant to this study since it is being used for exploration, development, production, field design, and management planning to improve decision-making, reduce costs, and speed up production. For establishing relationships between complex non-linear datasets, machine learning has proved superior to regression methods in petroleum engineering when it comes to high-dimensional data prediction errors, processing power, and memory. In this article, machine learning is compared with conventional statistical models of oil and gas engineering for determining and predicting reservoir pressure values in the development of oil fields. The effectiveness and potential of machine learning to determine reservoir pressure values was analysed. Using non-parametric multivariate model that link well performance over time, a new method is proposed for predicting reservoir pressure using machine learning. According to the proposed method, the predicted reservoir pressure correlates well with values measured by hydrodynamic studies of wells based on the dynamics of indicators describing well performance. Machine learning method based on random forest algorithm tends to provide better prediction reliability for reservoir pressure than linear regression method (absolute deviation: 0.86; relative deviation: 6.8%).

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“…1. Regression -prediction of a specific number based on an array of features or characteristics (Palmer 2009;Emeke 2019;S. Tou, 1988) ).…”

Section: Methodology and Materialsmentioning

confidence: 99%

Development of Oil Fields Using Science Artificial Intelligence and Machine Learning

Al-Obaidi,

Chang,

Hofmann

2023

nat

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“…These fractures, in conjunction with the porous layers, create a complex new medium system. Accurately assessing flow field characteristics and hydraulic conductivity of fractured multilayered porous media is crucial for CO 2 geological storage (Kim & Santamarina, 2015; March et al., 2018; Martinez et al., 2013; Noiriel & Daval, 2017; Vishal et al., 2014); groundwater contaminant transport (Cvetkovic et al., 2004; El‐Kharakany et al., 2022; Koohbor et al., 2020; Reagan et al., 2015; Vengosh et al., 2014); and hydrocarbon extraction (Hofmann et al., 2014; Hyman et al., 2016; Pan et al., 2020; Xu et al., 2021; X. C. Hu et al., 2021; Y. Q. Hu et al., 2021). Consequently, there is an urgent need to develop and promote a model that is both widely applicable and highly accurate for assessing the permeability characteristics of fractured multi‐layer porous media.…”

Section: Introductionmentioning

confidence: 99%

A Semi‐Analytical Solution for the Equivalent Permeability Coefficient of the Multilayered Porous Medium With Continuous Fracture

Zhang,

Liu

2024

Water Resources Research

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Fractures significantly alter the permeability characteristics of the natural porous media, especially for the multilayered porous medium system. The most direct and effective approach to assess the hydraulic conductivity of a multilayered porous medium with continuous fracture is to determine its equivalent permeability coefficient (EPC). A mathematical model with second‐order accuracy is established to analysis the permeability characteristics of the multilayered porous medium incorporating a continuous fracture. The Navier‒Stokes equation is used to govern the clear fluid motion in the continuous fracture, and the seepage fluid motion in the multilayered porous matrix is governed by the Brinkman‒Darcy equation. The semi‐analytical solutions for the velocity profile and EPC of the present model are derived under the conditions of the jumping stress at the fracture‐matrix interface and the continuous velocity at the matrix(i)‐matrix(i +1) interface. The Lattice Boltzmann method, finite element method, and one domain method simulations are conducted to verify the deduced semi‐analytical solutions of the velocity profile. Furthermore, the calculated EPC of the present multilayered model is in good agreement with that of the existing classic models (i.e., one region: fracture, two regions: fracture‐matrix, three regions: fracture‐matrix1‐matrix2, and multilayered porous medium without fracture). Parameter sensitivity reveals that increasing the thickness, dip angle, and porosity of the fractured porous media increases the flow velocity, while an increase in the stress jump coefficient can result in the opposite effect. This implies that porous layers containing fracture are more susceptible to erosion, particularly under conditions of steep dip angle and strong permeability.

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“…In a similar study, an alternate approach for rate/pressure deconvolution was presented. The physics-based trained parameters and algorithms play a key role in effectively implementing the recommended strategy by preserving superposition transient flow physics 6 . The primary drawback of this study is that this method fails to give satisfactory results when very highly variable and limited data is available.…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning in predicting oil rate decline for Bakken shale oil wells

Bhattacharyya

Vyas

2022

Sci Rep

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Commercial reservoir simulators are required to solve discretized mass-balance equations. When the reservoir becomes heterogeneous and complex, more grid blocks can be used, which requires detailed and accurate reservoir information, for e.g. porosity, permeability, and other parameters that are not always available in the field. Predicting the EUR (Estimated Ultimate Recovery) and rate decline for a single well can therefore take hours or days, making them computationally expensive and time-consuming. In contrast, decline curve models are a simpler and speedier option because they only require a few variables in the equation that can be easily gathered from the wells' current data. The well data for this study was gathered from the Montana Board of Oil and Gas Conservation's publicly accessible databases. The SEDM (Stretched Exponential Decline Model) decline curve equation variables specifically designed for unconventional reservoirs variables were correlated to the predictor parameters in a random oil field well data set. The study examined the relative influences of several well parameters. The study's novelty comes from developing an innovative machine learning (ML) (random forest (RF)) based model for fast rate-decline and EUR prediction in Bakken Shale oil wells. The successful application of this study relies highly on the availability of good quality and quantity of the dataset.

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A novel data-driven pressure/rate deconvolution algorithm to enhance production data analysis in unconventional reservoirs

Cited by 16 publications

References 12 publications

Development of Oil Fields Using Science Artificial Intelligence and Machine Learning

Development of Oil Fields Using Science Artificial Intelligence and Machine Learning

A Semi‐Analytical Solution for the Equivalent Permeability Coefficient of the Multilayered Porous Medium With Continuous Fracture

Application of machine learning in predicting oil rate decline for Bakken shale oil wells

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