Statistical Decline Curve Analysis for Automated Forecasting of Production from Coalbed Methane Wells

2018 ◽  
Author(s):  
Dan Kuznetsov ◽  
Glen Fernandes ◽  
Michael Daly ◽  
Kai Ma
Keyword(s):  
Coalbed Methane ◽  
Curve Analysis ◽  
Decline Curve Analysis ◽  
Decline Curve ◽  
Automated Forecasting

scholarly journals Evaluation of natural gas production optimization in Kailashtila gas field in Bangladesh using decline curve analysis method

2015 ◽  
Vol 50 (1) ◽  
pp. 29-38 ◽  
Author(s):  
MS Shah ◽  
HMZ Hossain
Keyword(s):  
Production System ◽  
Gas Production ◽  
Curve Analysis ◽  
Production Optimization ◽  
Reservoir Pressure ◽  
Gas Field ◽  
Wellhead Pressure ◽  
Decline Curve Analysis ◽  
Decline Curve ◽  
Overall Performance

Decline curve analysis of well no KTL-04 from the Kailashtila gas field in northeastern Bangladesh has been examined to identify their natural gas production optimization. KTL-04 is one of the major gas producing well of Kailashtila gas field which producing 16.00 mmscfd. Conventional gas production methods depend on enormous computational efforts since production systems from reservoir to a gathering point. The overall performance of a gas production system is determined by flow rate which is involved with system or wellbore components, reservoir pressure, separator pressure and wellhead pressure. Nodal analysis technique is used to performed gas production optimization of the overall performance of the production system. F.A.S.T. Virtu Well™ analysis suggested that declining reservoir pressure 3346.8, 3299.5, 3285.6 and 3269.3 psi(a) while signifying wellhead pressure with no changing of tubing diameter and skin factor thus daily gas production capacity is optimized to 19.637, 24.198, 25.469, and 26.922 mmscfd, respectively.Bangladesh J. Sci. Ind. Res. 50(1), 29-38, 2015

Decline Curve Analysis in Composite Reservoirs

1989 ◽  
Author(s):  
L. Turki ◽  
J.A. Demski ◽  
A.S. Grader
Keyword(s):  
Curve Analysis ◽  
Decline Curve Analysis ◽  
Decline Curve

Rescaling Method for Improved Machine-Learning Decline Curve Analysis for Unconventional Reservoirs

SPE Journal ◽  
2021 ◽  
pp. 1-14
Author(s):  
Boxiao Li ◽  
Travis C. Billiter ◽  
Timothy Tokar
Keyword(s):  
Machine Learning ◽  
Error Reduction ◽  
Curve Analysis ◽  
Support Vector ◽  
Mean Deviation ◽  
Production Forecast ◽  
Simple Method ◽  
Decline Curve Analysis ◽  
Curve Fit ◽  
Decline Curve

Summary Decline curve analysis (DCA) has been widely applied in production forecasting of wells in unconventional hydrocarbon reservoirs. However, traditional curve-fit-based methods fall short of forecast accuracy due to three weaknesses: first, they cannot capture the reservoir signals not modeled by the underlying DCA model formulas; second, when predicting the production of a target well, the production history of other wells in the geologic formation (which is valuable information) is not considered; third, the wells’ geographic, geologic, wellbore, well spacing, and completion properties, which are highly relevant to production capability, are not used. More recent approaches have begun replacing traditional DCA with machine-learning methods [e.g., random forest (RF), support vector regression (SVR), etc.] for production forecast. Nevertheless, these methods are still suboptimal in detecting similar production trends in different wells, leading to large forecast error. A new and simple method called dynamic production rescaling (DPR) is developed to improve the accuracy of machine-learning DCA (ML-DCA). By combining DPR with common ML-DCA methods, we observe that the error mean, deviation, and skewness can be significantly reduced by 15 to 35% compared with ML-DCA without DPR. The error reduction is 30 to 60% compared with automatic curve fit of the traditional modified Arps DCA model. DPR has been tested successfully on monthly production data of over 20,000 unconventional horizontal wells in the Permian and Appalachian basins for both long- and short-term forecasts. The significant error reduction is consistent across different basins and formations. DPR is computationally efficient, so a large number of wells can be analyzed automatically and quickly. Moreover, the effectiveness and efficiency of DPR is independent of the underlying machine-learning algorithm, further demonstrating its robustness.

GOR of Liquid-Rich Shale Reservoirs and its Application in Decline Curve Analysis

2017 ◽  
Author(s):  
M. Khoshghadam ◽  
A. Khanal ◽  
C. Yu ◽  
N. Rabinejadgangi ◽  
W. J. Lee
Keyword(s):  
Curve Analysis ◽  
Decline Curve Analysis ◽  
Decline Curve ◽  
Shale Reservoirs
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