Identifying and Estimating Desorption From Devonian Shale Gas Production Data

1989 ◽  
Author(s):  
H.S. Lane ◽  
A.T. Watson ◽  
D.E. Lancaster
Keyword(s):  
Shale Gas ◽  
Gas Production ◽  
Production Data ◽  
Devonian Shale

scholarly journals An Analytical Method for Parameter Interpretation of Fracture Networks in Shale Gas Reservoirs considering Uneven Support of Fractures

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Shijun Huang ◽  
Jiaojiao Zhang ◽  
Sidong Fang ◽  
Xifeng Wang
Keyword(s):  
Data Analysis ◽  
Shale Gas ◽  
Analytical Method ◽  
Material Balance ◽  
Gas Production ◽  
Production Data ◽  
Analysis Method ◽  
Gas Reservoirs ◽  
Data Analysis Method ◽  
Parameter Interpretation

In shale gas reservoirs, the production data analysis method is widely used to invert reservoir and fracture parameter, and productivity prediction. Compared with numerical models and semianalytical models, which have high computational cost, the analytical model is mostly used in the production data analysis method to characterize the complex fracture network formed after fracturing. However, most of the current calculation models ignore the uneven support of fractures, and most of them use a single supported fracture model to describe the flow characteristics, which magnifies the role of supported fracture to a certain extent. Therefore, in this study, firstly, the fractures are divided into supported fractures and unsupported fractures. According to the near-well supported fractures and far-well unsupported fractures, the SRV zone is divided into outer SRV and inner SRV. The four areas are characterized by different seepage models, and the analytical solutions of the models are obtained by Laplace transform and inverse transform. Secondly, the material balance pseudotime is introduced to process the production data under the conditions of variable production and variable pressure. The double logarithmic curves of normalized production rate, rate integration, the derivative of the integration, and material balance pseudotime are established, and the parameters are interpreted by fitting the theoretical curve to the measured data. Then, the accuracy of the method is verified by comparison the parameter interpretation results with well test results, and the influence of parameters such as the half-length and permeability of supported and unsupported fractures on gas production is analyzed. Finally, the proposed method is applied to four field cases in southwest China. This paper mainly establishes an analytical method for parameter interpretation after hydraulic fracturing based on the production data analysis method considering the uneven support of fractures, which is of great significance for understanding the mechanism of fracturing stimulation, optimization of fracturing parameters, and gas production forecast.

scholarly journals Development of Shale Gas Prediction Models for Long-Term Production and Economics Based on Early Production Data in Barnett Reservoir

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 424 ◽  
Author(s):  
Viet Nguyen-Le ◽  
Hyundon Shin ◽  
Edward Little
Keyword(s):  
Shale Gas ◽  
Net Present Value ◽  
Prediction Models ◽  
Input Parameter ◽  
Gas Production ◽  
Barnett Shale ◽  
Production Data ◽  
Production History ◽  
Early Production

This study examined the relationship between the early production data and the long-term performance of shale gas wells, including the estimated ultimate recovery (EUR) and economics. The investigated early production data are peak gas production rate, 3-, 6-, 12-, 18-, and 24-month cumulative gas production (CGP). Based on production data analysis of 485 reservoir simulation datasets, CGP at 12 months (CGP_12m) was selected as a key input parameter to predict a long-term shale gas well’s performance in terms of the EUR and net present value (NPV) for a given well. The developed prediction models were then validated using the field production data from 164 wells which have more than 10 years of production history in Barnett Shale, USA. The validation results showed strong correlations between the predicted data and field data. This suggests that the proposed models can predict the shale gas production and economics reliably in Barnett shale area. Only a short history of production (one year) can be used to estimate the EUR and NPV of various production periods for a gas well. Moreover, the proposed prediction models are consistently applied for young wells with short production histories and lack of reservoir and hydraulic fracturing data.

Prediction of Shale-Gas Production at Duvernay Formation Using Deep-Learning Algorithm

SPE Journal ◽  
2019 ◽  
Vol 24 (06) ◽  
pp. 2423-2437 ◽  
Author(s):  
Kyungbook Lee ◽  
Jungtek Lim ◽  
Daeung Yoon ◽  
Hyungsik Jung
Keyword(s):  
Time Series ◽  
Shale Gas ◽  
Transient Flow ◽  
Time Series Data ◽  
Gas Production ◽  
Series Data ◽  
Production Data ◽  
Gas Wells ◽  
Future Production ◽  
Unseen Data

Summary Decline–curve analysis (DCA) is an easy and fast empirical regression method for predicting future well production. However, applying DCA to shale–gas wells is limited by long transient flow, a unique completion design, and high–density drilling. Recently, a long short-term-memory (LSTM) algorithm has been widely applied to the prediction of time–series data. Because shale–gas–production data are time–series data, the LSTM algorithm can be applied to predict future shale–gas production. After information for 332 shale–gas wells in Alberta, Canada, is obtained from a commercial database, the data are preprocessed in seven steps, including cutoffs for well list, data cleaning, feature extraction, train and test sets split, normalization, and sorting for input into the LSTM model. The LSTM model is trained in 405 seconds by two features of production data and a shut–in (SI) period from 300 wells. The two–feature case shows a better prediction accuracy than both the one–feature case (i.e., production data only) and the hyperbolic DCA, where the three methods are tested on unseen data from 15 wells. The two–feature case can predict future production rates according to the SI period and provide a stable result for available time–series data.

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