scholarly journals Shale gas well productivity potential evaluation based on data-driven methods: case study in the WY block

2022 ◽  
Author(s):  
Chaodong Tan ◽  
Hanwen Deng ◽  
Wenrong Song ◽  
Huizhao Niu ◽  
Chunqiu Wang
Keyword(s):  
Shale Gas ◽  
Production Optimization ◽  
Data Driven ◽  
Operating Parameters ◽  
Geological Condition ◽  
Gas Wells ◽  
Gas Well ◽  
Geological Conditions ◽  
Potential Evaluation ◽  
Productivity Potential

AbstractEvaluating the productivity potential of shale gas well before fracturing reformation is imperative due to the complex fracturing mechanism and high operation investment. However, conventional single-factor analysis method has been unable to meet the demand of productivity potential evaluation due to the numerous and intricate influencing factors. In this paper, a data-driven-based approach is proposed based on the data of 282 shale gas wells in WY block. LightGBM is used to conduct feature ranking, K-means is utilized to classify wells and evaluate gas productivity according to geological features and fracturing operating parameters, and production optimization is realized through random forest. The experimental results show that shale gas productivity potential is basically determined by geological condition for the total influence weights of geologic properties take the proportion of 0.64 and that of engineering attributes is 0.36. The difference between each category of well is more obvious when the cluster number of well is four. Meanwhile, those low production wells with good geological conditions but unreasonable fracturing schemes have the greatest optimization space. The model constructed in this paper can classify shale gas wells according to their productivity differences, help providing suggestions for engineers on productivity evaluation and the design of fracturing operating parameters of shale gas well.

scholarly journals Methane in groundwater before, during, and after hydraulic fracturing of the Marcellus Shale

2018 ◽  
Vol 115 (27) ◽  
pp. 6970-6975 ◽  
Author(s):  
E. Barth-Naftilan ◽  
J. Sohng ◽  
J. E. Saiers
Keyword(s):  
Groundwater Quality ◽  
Shale Gas ◽  
Marcellus Shale ◽  
Gas Wells ◽  
Local Flow ◽  
Gas Well ◽  
Monitoring Wells ◽  
Regional Flow ◽  
Flow Systems ◽  
Methane Concentrations

Concern persists over the potential for unconventional oil and gas development to contaminate groundwater with methane and other chemicals. These concerns motivated our 2-year prospective study of groundwater quality within the Marcellus Shale. We installed eight multilevel monitoring wells within bedrock aquifers of a 25-km2 area targeted for shale gas development (SGD). Twenty-four isolated intervals within these wells were sampled monthly over 2 years and groundwater pressures were recorded before, during, and after seven shale gas wells were drilled, hydraulically fractured, and placed into production. Perturbations in groundwater pressures were detected at hilltop monitoring wells during drilling of nearby gas wells and during a gas well casing breach. In both instances, pressure changes were ephemeral (<24 hours) and no lasting impact on groundwater quality was observed. Overall, methane concentrations ([CH4]) ranged from detection limit to 70 mg/L, increased with aquifer depth, and, at several sites, exhibited considerable temporal variability. Methane concentrations in valley monitoring wells located above gas well laterals increased in conjunction with SGD, but CH4 isotopic composition and hydrocarbon composition (CH4/C2H6) are inconsistent with Marcellus origins for this gas. Further, salinity increased concurrently with [CH4], which rules out contamination by gas phase migration of fugitive methane from structurally compromised gas wells. Collectively, our observations suggest that SGD was an unlikely source of methane in our valley wells, and that naturally occurring methane in valley settings, where regional flow systems interact with local flow systems, is more variable in concentration and composition both temporally and spatially than previously understood.

Pressure-Dependent Natural-Fracture Permeability in Shale and Its Effect on Shale-Gas Well Production

2013 ◽  
Vol 16 (02) ◽  
pp. 216-228 ◽  
Author(s):  
Y.. Cho ◽  
O.G.. G. Apaydin ◽  
E.. Ozkan
Keyword(s):  
Shale Gas ◽  
Flow Model ◽  
Natural Fracture ◽  
Fracture Permeability ◽  
Gas Wells ◽  
Gas Well ◽  
Fracture Conductivity ◽  
Pressure Drops ◽  
Well Production ◽  
The Matrix

Summary This paper presents an investigation of the effect of pressure-dependent natural-fracture permeability on production from shale-gas wells. The motivation of the study is to provide data for the discussion of whether it is crucial to pump proppant into natural fractures in shale plays. Experiments have been conducted on Bakken-shale core samples to select appropriate correlations to represent fracture conductivity as a function of pressure (the actual characterization of fracture conductivity under stress for a specific formation is not an objective of the study). Correlations have been used in a flow model to demonstrate the potential impact of natural-fracture closure as pressure drops during production. Although the correlations indicate up to an 80% reduction in fracture permeability over practical ranges of pressure, the results of the flow model do not warrant the claims that fracture closing plays a significant role in the productivity losses of shale-gas wells. A history match of the performances of two wells in the Barnett and Haynesville formations also indicates that the effect of pressure-dependent natural-fracture permeability on shale-gas-well production is a function of the permeability of the matrix system. If the matrix system is too tight, then the retained permeability of the natural fractures may still be sufficient for the available volume of the fluid when the system pressure drops.

scholarly journals A Production Prediction Method for Shale Gas Wells Based on Multiple Regression

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1461
Author(s):  
Wente Niu ◽  
Jialiang Lu ◽  
Yuping Sun
Keyword(s):  
Prediction Model ◽  
Multiple Regression ◽  
Shale Gas ◽  
Evaluation Method ◽  
Pearson Correlation ◽  
Controlling Factors ◽  
Accurate Prediction ◽  
Gas Wells ◽  
Gas Well ◽  
Training Samples

The estimated ultimate recovery (EUR) of a single shale gas well is one of the important evaluation indicators for the scale and benefit development of shale gas, which is affected by many factors such as geological and engineering, so its accurate prediction is difficult. In order to realize the accurate prediction of ultimate recovery, this study considered 172 shale gas wells in the Weiyuan block as samples and selected 19 geological and engineering factors that affect the ultimate recovery of shale gas wells. Furthermore, eight key controlling factors were selected by means of the Pearson correlation coefficient and maximum mutual information coefficient comprehensive evaluation method. The data were divided into training and testing samples. Different numbers of training samples were selected and seven schemes were designed. Based on the key controlling factors, the ultimate recovery prediction model for shale gas wells in this block was established through multiple regression methods. The effectiveness of the prediction model was verified by analyzing the testing samples. The result shows that with the increase of the size of training samples, the error of the ultimate recovery predicted by the model gradually decreases gradually. When predicting the single gas well, the average absolute error of ultimate recovery is less than 20% if the number of the training gas well is more than 80. When analyzing the development potential of similar blocks without drilling, the error of the sum of ultimate recovery is less than 10% if the size of the training gas well reaches 60.

scholarly journals Reasonable Pumping Depth for Drainage and Gas Recovery of Shale Gas Wells

2020 ◽  
Vol 38 (3) ◽  
pp. 701-707
Author(s):  
Jianda Chen ◽  
Dajiang Wang ◽  
Zhiquan Zhang ◽  
Jie Liu
Keyword(s):  
Shale Gas ◽  
Design Method ◽  
Technological Parameters ◽  
Gas Wells ◽  
Gas Well ◽  
Gas Recovery ◽  
Wellbore Pressure ◽  
Current Design ◽  
Ultimate Purpose ◽  
Production Characteristics

For shale gas wells, in the initial production stage, the liquid production is large, and the lifting process is needed to assist the drainage. However, for gas wells, especially shale gas wells, the ultimate purpose is different from that of oil wells, and the current design method of pumping depth cannot meet the field requirements. Starting from the production characteristics of liquid-producing gas wells, this paper analyzed the gas well productivity, wellbore pressure distribution and critical liquid-carrying flow, and adopted the node analysis method to propose a design method for the pumping depth of shale gas wells during drainage and gas recovery. Then, the proposed method was applied to optimize the design of the jet pump of well A in Block JY, according to the design results, the pump was started for production; after the wellbore liquid level was raised to the designed depth, the gas well can conduct annulus space liquid-carrying production, and the production effect of well A showed that, the proposed method can be applied as a method for optimizing the technological parameters of shale gas wells.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

A Methodology to Quantify the Volume-of-investigation in DFIT Design for Shale Gas Wells

2019 ◽  
Author(s):  
Zhiming Chen ◽  
Hongyang Chu ◽  
Xuefeng Tang ◽  
Lingyu Mu ◽  
Peng Dong ◽  
...  
Keyword(s):  
Shale Gas ◽  
Gas Wells
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