The Utilization of the "Rate-Integral" to Assist with Decline Curve Analysis of Poor-Quality Unconventional Time-Rate Data

2021 ◽  
Author(s):  
E.W. Bryan ◽  
D. Symmons ◽  
D. Ilk ◽  
T. A. Blasingame
Keyword(s):  
Poor Quality ◽  
Curve Analysis ◽  
Rate Data ◽  
Time Rate ◽  
Decline Curve Analysis ◽  
Decline Curve

Permanent Gauge Pressure and Rate Measurements for Reservoir Description and Well Monitoring: Field Cases

1998 ◽  
Vol 1 (03) ◽  
pp. 224-230 ◽  
Author(s):  
Trond Unneland ◽  
Yves Manin ◽  
Fikri Kuchuk
Keyword(s):  
Pressure Gauge ◽  
Pressure Sensors ◽  
Curve Analysis ◽  
Rate Data ◽  
Well Test ◽  
Reserve Estimation ◽  
Decline Curve Analysis ◽  
Decline Curve ◽  
Reservoir Description ◽  
Interference Tests

Summary This paper presents a procedure for interpreting data acquired with permanent downhole pressure sensors in association with surface or downhole rate measurements. The usefulness of this data source in reservoir description and well performance monitoring is illustrated. Unlike previously published examples, the interpretation is based on the analysis on a stream of data acquired over large periods of time, thus utilizing the continuous nature of the measurements. Three field cases are presented using the pressure and rate data in decline-curve analysis for wells with a variable downhole flowing pressure, and through more sophisticated models that are similar to the ones used in well test analysis. Because such interpretation is conducted while continuing production, it is particularly well suited for a well or group of wells under extended testing, which are equipped with downhole gauges and are flowing through surface separation and metering systems. Wells completed with both permanent downhole rate and pressure measurements are also ideal candidates for this type of analysis. Finally, the influence of the pressure sensor long term drift and the rate measurement error on the interpretation results and future forecasts are investigated. Introduction Since the first permanent downhole gauge installations in the early 1960's on land wells, the new technology in cable manufacturing, gauge sensor and electronics has permitted reliable installations also in hot, deep wells and subsea completions. These systems have gained acceptance among operators, and currently several hundred downhole gauges are installed every year. The traditional applications associated with permanent downhole systems can be characterized by four distinctions:single well optimization,reservoir description,safety improvement, andoperating cost reduction. Combining the recent technology development and these applications, the downhole gauge installations can be safe and reliable, as well as good investments. Most of the previous papers on the subject have focused on the hardware involved in permanent downhole pressure gauge installations. Regarding reservoir description, a few examples have been published where data recorded by the permanent downhole gauges have been used in well test transient analysis and multiwell interference tests. However, little has been published on the use of continuous downhole measurement in order to enhance reservoir description when associated with rate data during the pseudosteady state or depletion period of a field or a separate block. Decline curve analysis is one of the most widely used and documented methods for reserve estimation and production forecasting for a field under depletion. Solutions have been published for the case of a well producing at constant downhole flowing pressure. In reality, due to production constraints or change in operating procedures, the downhole flowing pressure seldom remains at a constant level over long periods of time. In the decline curve analysis literature, various methods have been proposed to account for these pressure variations; these include normalization and various types of superposition based on the pressure change observed at the wellhead.

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.

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