Smart shale gas production performance analysis using machine learning applications

Syed, Fahad I.; Alnaqbi, Salem; Muther, Temoor; Dahaghi, Amirmasoud Kalantari; Negahban, Shahin

doi:10.1016/j.ptlrs.2021.06.003

Cited by 37 publications

(17 citation statements)

References 16 publications

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“…With the help of ML simulation and modeling, the exploitation characteristics of shale oil/gas reservoirs are quickly analyzed, saving the exploration cost. − Due to the shale oil/gas production characteristics of rapid decay and gradual recovery, various parameters such as oil/gas well location, geological conditions, petrophysics, etc., must be considered comprehensively . Hence, oil/gas production is challenging to be predicted even with ML-based methods . In addition, the training of ML models requires massive data from shale oil/gas reservoirs.…”

Section: Reconstruction Methods Of Kerogen Modelmentioning

confidence: 99%

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Perspectives of Machine Learning Development on Kerogen Molecular Model Reconstruction and Shale Oil/Gas Exploitation

Kang

Zhao

2022

Energy Fuels

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The shale revolution has provided abundant shale oil/gas resources for the world, but the efficient, sustainable, and environmentally friendly exploitation of shale oil/gas is still challenging. Kerogen is the primary hydrocarbon source of shale oil/gas. The research on the kerogen chemo-mechanical properties significantly influences the development of shale oil/gas extraction technology. Rapid reconstruction of the kerogen molecular models is the most effective way to study the generation mechanism of shale oil/gas from the bottom-up molecular level. However, due to the combinatorial explosion problem, the reconstruction complexity of kerogen increases sharply because of the kerogen’s characteristics of complex origin, large molecular weight, and diverse functional groups. The traditional kerogen molecular reconstruction methods require professionals to comprehensively analyze various experimental information to approximate the actual kerogen molecular models through trial-and-error. So, the traditional methods are time and material-consuming and extremely inefficient. These shortcomings make researchers spend too much strength on the reconstruction of kerogen molecular models and cannot focus on the study of kerogen chemo-mechanical properties. For the past few years, state-of-the-art machine learning (ML) methods have been applied to intelligently reconstruct the kerogen molecular models through high-throughput and predict shale oil/gas production mechanisms. Although the current work is still in the infancy stage, ML methods are believed to be the most promising way to solve the drawbacks of traditional methods and reconstruct kerogen in reliable and large molecular weight. Hence, mechano-energetics is proposed to study the efficient development and utilization of energy based on mechanics and ML. This paper briefly reviews the development history of kerogen molecular model reconstruction methods and the research of ML in the fields of kerogen reconstruction and shale oil/gas exploitation. Some recommendations for further ML-based work are also suggested. We are convinced that the ML methods will accelerate the research of kerogen and promote the significant development of unconventional oil/gas exploitation technologies.

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Section: Reconstruction Methods Of Kerogen Modelmentioning

confidence: 99%

“…149 Hence, oil/gas production is challenging to be predicted even with ML-based methods. 150 In addition, the training of ML models requires massive data from shale oil/gas reservoirs. Collecting and labeling a large number of qualified training samples is also a very tough task.…”

Section: Kerogen Molecular Model Reconstruction By ML Methodsmentioning

confidence: 99%

Perspectives of Machine Learning Development on Kerogen Molecular Model Reconstruction and Shale Oil/Gas Exploitation

Kang

Zhao

2022

Energy Fuels

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“…With the increase of demand in shale reservoirs, a review study was done by Syed 13 to model petrophysical and geo-mechanical properties using ML techniques. In continuation of this, another study on recent ML based models for the estimation of production performance of shale gas reservoirs was done by Syed 12 . Various key parameters and ML algorithms used to forecast the shale gas reservoir’s production behavior were discussed along with their advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Analysis of environmental factors using AI and ML methods

Haq

Ahmed

Khan

et al. 2022

Sci Rep

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The main goal of this research paper is to apply a deep neural network model for time series forecasting of environmental variables. Accurate forecasting of snow cover and NDVI are important issues for the reliable and efficient hydrological models and prediction of the spread of forest. Long Short Term Memory (LSTM) model for the time series forecasting of snow cover, temperature, and normalized difference vegetation index (NDVI) are studied in this research work. Artificial neural networks (ANN) are widely used for forecasting time series due to their adaptive computing nature. LSTM and Recurrent neural networks (RNN) are some of the several architectures provided in a class of ANN. LSTM is a kind of RNN that has the capability of learning long-term dependencies. We followed a coarse-to-fine strategy, providing reviews of various related research materials and supporting it with the LSTM analysis on the dataset of Himachal Pradesh, as gathered. Environmental factors of the Himachal Pradesh region are forecasted using the dataset, consisting of temperature, snow cover, and vegetation index as parameters from the year 2001–2017. Currently, available tools and techniques make the presented system more efficient to quickly assess, adjust, and improve the environment-related factors analysis.

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“…Due to the complex matrix structure with low permeabilities and porosities, the shale gas is trapped inside the nano-pores and hence the flow is highly restricted. To produce gas from such a complex system, an interconnected fracture system with extended wells is required (Ayers et al 2012;Sprunger et al 2021;Syed et al 2021a;Zhang et al 2020). Horizontal drilling, enhanced oil recovery, and hydraulic fracturing are some of the technologies implemented to recover unconventional hydrocarbon reservoirs (Muther et al 2021(Muther et al , 2022Syed et al 2011Syed et al , 2021aYue et al 2020).…”

Section: Introductionmentioning

confidence: 99%

“…To produce gas from such a complex system, an interconnected fracture system with extended wells is required (Ayers et al 2012;Sprunger et al 2021;Syed et al 2021a;Zhang et al 2020). Horizontal drilling, enhanced oil recovery, and hydraulic fracturing are some of the technologies implemented to recover unconventional hydrocarbon reservoirs (Muther et al 2021(Muther et al , 2022Syed et al 2011Syed et al , 2021aYue et al 2020). Specifically, the horizontal well coupled with multistaged hydraulic fractures has been used quite extensively in enhancing the permeabilities of ultra-tight Marcellus shale matrix (Yu and Sepehrnoori 2014b).…”

Section: Introductionmentioning

confidence: 99%

Contribution of gas adsorption–desorption in Marcellus shale for different fractured well configurations

Muther

Syed

Dahaghi

et al. 2022

J Petrol Explor Prod Technol

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Gas adsorption onto the shale system carries significant importance in accurately forecasting gas production and estimating underground reserves. For the Marcellus shale system, the adsorption of gas also plays a critical role in ultimate recovery and overall reserves quantification. Yet, the effect of different adsorption characteristics in different Marcellus shale regions is not systematically analyzed together. In this study, the contribution of different gas adsorption phenomena in Marcellus shale for different fractured well configurations is investigated. The objective is to understand the reservoir production responses under various adsorption characteristics and well design. For this analysis, a mechanistic Marcellus shale model under confining stresses is numerically simulated with the available literature data. After that, six samples containing adsorption characteristics of different Marcellus shale regions are taken from the literature and specified in the model for accurately defining the adsorption physics in the shale system. In the end, two different well configurations including the fractured vertical and horizontal well are specified in the model separately to analyze the impact of gas desorption on production response. The analysis indicates that the gas desorption improves the overall gas production by a maximum of 5% in a single-stage multi-clustered fractured horizontal well. In addition, the effect of desorption is found to be minimal during initial flow periods, and considerable at longer flow periods. Additionally, the gas desorption is found to be more responsive towards high surface area and large fracture networks. Finally, it is determined that a fractured horizontal well is a viable option that allows high gas desorption in Marcellus shale. This study, hence, aids widely in deciding better production strategies based on adsorption characteristics for producing Marcellus shale.

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Smart shale gas production performance analysis using machine learning applications

Cited by 37 publications

References 16 publications

Perspectives of Machine Learning Development on Kerogen Molecular Model Reconstruction and Shale Oil/Gas Exploitation

Perspectives of Machine Learning Development on Kerogen Molecular Model Reconstruction and Shale Oil/Gas Exploitation

Analysis of environmental factors using AI and ML methods

Contribution of gas adsorption–desorption in Marcellus shale for different fractured well configurations

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