Hong, Aojie (National IOR Centre of Norway and University of Stavanger) | Bratvold, Reidar B. (National IOR Centre of Norway and University of Stavanger) | Lake, Larry W. (University of Texas at Austin) | Ruiz Maraggi, Leopoldo M. (University of Texas at Austin)
Aojie Hong and Reidar B. Bratvold, National IOR Centre of Norway and University of Stavanger, and Larry W. Lake and Leopoldo M. Ruiz Maraggi, University of Texas at Austin Summary Decline-curve analysis (DCA) for unconventional plays requires a model that can capture the characteristics of different flow regimes. Thus, various models have been proposed. Traditionally, in probabilistic DCA, an analyst chooses a single model that is believed to best fit the data. However, several models might fit the data almost equally well, and the one that best fits the data might not best represent the flow characteristics. Therefore, uncertainty remains regarding which is the "best" model. This work aims to integrate model uncertainty in probabilistic DCA for unconventional plays. Instead of identifying a single "best" model, we propose to regard any model as potentially good, with goodness characterized by a probability. The probability of a model being good is interpreted as a measure of the relative truthfulness of this model compared with the other models. This probability is subsequently used to weight the model forecast. Bayes' law is used to assess the model probabilities for given data. Multiple samples of the model-parameter values are obtained using maximum likelihood estimation (MLE) with Monte Carlo simulation. Thus, the unique probabilistic forecasts of each individual model are aggregated into a single probabilistic forecast, which incorporates model uncertainty along with the intrinsic uncertainty (i.e., the measurement errors) in the given data. We demonstrate and conclude that using the proposed approach can mitigate over/underestimates resulting from using a single decline-curve model for forecasting. The proposed approach performs well in propagating model uncertainty to uncertainty in production forecasting; that is, we determine a forecast that represents uncertainty given multiple possible models conditioned to the data. The field data show that no one model is the most probable to be good for all wells. The novelties of this work are that probability is used to describe the goodness of a model; a Bayesian approach is used to integrate the model uncertainty in probabilistic DCA; the approach is applied to actual field data to identify the most-probable model given the data; and we demonstrate the value of using this approach to consider multiple models in probabilistic DCA for unconventional plays. Introduction Although numerical techniques for forecasting hydrocarbon production have developed rapidly over the past decades, DCA remains an industry-accepted method and is used extensively in the oil and gas industry. Decline-curve models are very computationally attractive because only production data, which can be easily acquired, are required for determining a few parameter values through history matching.
Hong, Aojie (University of Stavanger and The National IOR Centre of Norway) | Bratvold, Reidar B. (University of Stavanger and The National IOR Centre of Norway) | Lake, Larry W. (The University of Texas at Austin)
This paper was prepared for presentation at the Unconventional Resources Technology Conference held in Houston, Texas, USA, 23-25 July 2018. The URTeC Technical Program Committee accepted this presentation on the basis of information contained in an abstract submitted by the author(s). The contents of this paper have not been reviewed by URTeC and URTeC does not warrant the accuracy, reliability, or timeliness of any information herein. All information is the responsibility of, and, is subject to corrections by the author(s). Any person or entity that relies on any information obtained from this paper does so at their own risk. The information herein does not necessarily reflect any position of URTeC. Any reproduction, distribution, or storage of any part of this paper by anyone other than the author without the written consent of URTeC is prohibited. Abstract Decline curve analysis (DCA) for unconventional plays requires a model that can capture the characteristics of different flow regimes. Thus, various models have been proposed. Traditionally, in probabilistic DCA, an analyst chooses a single model that he/she believes best fits the data. However, several models might fit the data almost equally well and the one that best fits the data might not best represent the flow characteristics. Therefore, uncertainty in which is the "best" model remains. This work aims to integrate model uncertainty in probabilistic DCA for unconventional plays. Instead of identifying a single "best" model, we propose to regard any model as potentially good, where goodness is characterized by a probability.
Hong, Aojie (The National IOR Centre of Norway and University of Stavanger) | Bratvold, Reidar B. (The National IOR Centre of Norway and University of Stavanger) | Lake, Larry W. (University of Texas at Austin)
Simple or proxy production models are potentially very useful and tractable because they are computationally attractive whilst still providing insight to the decision at hand. Useful and tractable models are required for supporting high-quality decisions in uncertain, complex, and computationally demanding contexts. A key decision for development planning is: what is the optimal time to initiate an Improved-Oil-Recovery (IOR) process. We aim to illustrate the implementation and application of a useful and tractable approach for the analysis of the optimal IOR switch time using a two-factor production model and Least-Squares Monte Carlo (LSM) simulation.
The two-factor production model contains only two parameters for each recovery phases. One parameter describes how much recovery efficiency a recovery mechanism can ultimately achieve whilst the other describes how fast the recovery efficiency increases. The simplicity of the model makes it computationally attractive. The LSM algorithm is an approximate dynamic programming approach, which allows for learning over time. It provides a near-optimal solution for the IOR switch time problem. The Value-Of-Information (VOI) framework—a powerful decision-analysis tool—provides an estimate of the value of learning.
Closed-Loop Reservoir Management (CLRM) is considered to be a state-of-the-art approach to solving for the optimal IOR switch time. However, this approach can produce a suboptimal solution as the CLRM approach considers only uncertainties and actions reflecting currently available information but not those uncertainties and actions arising from future information. The dynamic programming approach used here considers both the impact of the information obtained before a decision is made and the impact of the information that might be obtained to support future decisions. We conclude that a dynamic programming approach, such as the LSM algorithm, can significantly improve both the timing and value of decisions, leading to a significant increase in a field's economic performance. Furthermore, the two-factor model combined with the LSM algorithm is tractable and provides useful insight into the IOR switch time problem.
The novelties provided by this work are: developing and illustrating the structure of the IOR switch time problem in a decision tree, demonstrating and discussing the suboptimality of the CLRM solution, developing and illustrating the detailed steps of applying the LSM algorithm for the IOR switch time decision, and implementing the two-factor model combined with the LSM algorithm for analyzing the optimal IOR switch time.