Uncertainty Analysis of Production Forecast in Permian Basin

摘要

This paper evaluates the impact of decision making and uncertainty associated with production forecast for 2000+ wells completed in Permian basin. Existing studies show that unconventional reservoirs have complex reservoir characteristics making traditional methods for ultimate recovery estimation insufficient. Based on these limitations, uncertainty is increased during the estimation of reservoir properties, reserve quantification and, evaluation of economic viability. Thus, it is necessary to determine and recommend favorable conditions in which these reservoirs are developed. In this study, cumulative production is predicted using four different decline curve analysis (DCA) . power law exponential, stretched exponential, extended exponential and Duong models. A comparison between the predicted cumulative production from the models using a subset of historical data (0-3months) and actual production data observed over the same time period determines the accuracy of DCA's; repeating the evaluation for subsequent time intervals (0-6 months, 0-9 months,) provides a basis to monitor the performance of each DCA with time. Moreover, the best predictive models as a combination of DCA's predictions is determined via multivariate regression. Afterwards, uncertainty due to prediction errors excluding any bias is estimated and expected disappointment (ED) is calculated using probability density function on the results obtained. In this paper, uncertainty is estimated from the plot of ED versus time for all wells considered. ED drops for wells having longer production history as more data are used for estimation. Also, the surprise/ disappointment an operator experiences when using various DCA methods is estimated for each scenario. However, it appears that whilst Duong (DNG) method always overpredicts, power law exponential (PLE) decline mostly under predicts, the stretched exponential lies between DNG & PLE estimates and the extended exponential DCA demonstrates an erratic behavior crossing over the actual trend multiple times with time. In conclusion, profitability zones for producing oil in the Permian basin are defined implicitly based on drilling and completion practices which paves the path to determine the "sweet spot" via optimization of fracture spacing and horizontal length in the wells. The outcome of the paper helps improve the industry's take on uncertainty analysis in production forecast, especially the concept of expected disappointment/surprise. This study suggests that effects of bias due to decision making can be much greater than what has often regarded, which can change the performance evaluation of the Permian basin in terms of economic feasibility.