Summary Production-data analysis is a practice fraught with inconsistencies. In the application of any single model, the quantity of answers arrived at by experienced evaluators is often equal to the number of evaluators analyzing the data. The cause of such inconsistency is bias on the part of evaluators. Although the colloquial use of bias typically implies systematic error, in this paper, we define bias as an expression of belief by the evaluator. With the lack of recognition of bias, no means exists with which to gauge its accuracy. A method that requires explicit expression of one's bias in time/rate decline behavior can provide an objective means with which to evaluate it. In this work, we present a machine-learning method to forecast production in unconventional, liquid-rich shale and gas-shale wells. Methods were developed for probabilistic decline-curve analysis with Markov-chain Monte Carlo simulation (MCMC) as a means to quantify reserves uncertainty, to incorporate prior information (i.e., bias), and to do so quickly. We extend the existing approaches by (a) a modified likelihood-distribution function to improve “learning” of production data, (b) integration of the transient hyperbolic model (THM) to explicitly define the various flow regimes present in unconventional wells, (c) a method for construction of discretized “percentile neighborhood” forecasts, and (d) construction of type wells from an analyzed well population. The accuracy and calibration of the method are demonstrated by an analysis of 136 wells in the Elm Coulee Field of the Bakken. Quantification of change in time/rate behavior caused by completion design, and the inference of physical behavior and properties, is demonstrated with a tight oil play in the Cleveland sand formation of the Anadarko Basin, as well as a shale play in the Wolfcamp formation of the Permian Basin. We show that this implementation of supervised machine learning, in combination with well-calibrated bias, improves the estimation of uncertainty of the posterior distribution of forecasts. In addition, hindcasts performed at various time intervals result in accurate estimation of mean five-year cumulative production. We observe that the “percentile neighborhood” forecasts are reasonable fits of production data comparable to those that may be created by a human evaluator, and that the type well computed is representative of the decline behavior of the well population upon which it is based. We conclude that, given the speed and accuracy of the process, machine learning is a reliable technology as defined by the US Securities and Exchange Commission (SEC), and can significantly improve the process of production forecasting by human evaluators for most unconventional wells with consistent trends of production history.

Production Data Analysis is a practice wrought with inconsistencies. In the application of any single model, the quantity of answers arrived at by experienced evaluators is often equal to the number of evaluators analyzing the data. The cause of such inconsistency is bias on the part of evaluators. While the colloquial use of bias typically implies systematic error, in this paper we define bias as an expression of belief by the evaluator. With the lack of recognition of bias, no manner exists in which to gauge its accuracy. A method that requires explicit expression of one's bias in rate-time decline behavior can provide an objective manner in which to evaluate it. In this work, we present a machine learning method to forecast production in unconventional, liquids-rich shale and gas shale wells. Gong et al [2011] developed a method for probabilistic decline curve analysis using Markov-chain Monte Carlo simulation (MCMC) as a means to quantify reserves uncertainty, incorporate prior information (i.e. bias), and to do so quickly. However, their approach resulted in limited use of discrete P10, P50, & P90 production forecasts, as these often did not align with production data. We extend their approach by a) utilizing the Transient Hyperbolic Model (THM) to represent the various flow regimes present in unconventional wells, b) a methods for construction of representative "percentile neighborhood" forecasts, c) construction of Type Curves from an analyzed well set, and d) a modified likelihood algorithm to improve the accuracy of discrete forecasts. The accuracy and calibration of the method is demonstrated by an analysis of 136 wells in the Elm Coulee Field of the Bakken. Quantification of change in rate-time behavior due to completion design, and the inference physical behavior and properties, is demonstrated using a tight oil play in the Cleveland sand formation of the Anadarko Basin, and a shale play in the Wolfcamp formation of the Permian Basin. We show that this implementation of supervised machine learning, in combination with well-calibrated bias, improves the estimation of uncertainty of the distribution of forecasts. Additionally, hindcasts performed at various time intervals results in accurate Mean 5 year cumulative production. We observe that the "percentile neighborhood" forecasts are reasonable fits of production data comparable to those that may be created by a human evaluator, and that the type curve computed is representative of the decline behavior of the wells upon which it is based. We conclude that, given the speed and accuracy of the process, machine learning is a reliable technology as defined by the SEC, and can replace the process of manual production forecasting by human evaluators for most unconventional wells with consistent trends of production history.

Choosing the best projects to fund is easy. Our challenge is trying to weigh the complexities of the projects that are at the threshold for funding in a capital-constrained environment. Even though all the projects under consideration might be economically viable on a stand-alone basis, we seek to determine which suite of capital funding options best meets our long-term goals. Apache Corporation maintains a broad inventory of operating assets and investment opportunities, composed of projects with considerable variability with regard to uncertainty in potential performance and risk of financial loss. The opportunity suite consists of projects with highly varying capital investment patterns and production profiles. Further, the project portfolio faces varying exposure to commodity markets, petroleum fiscal regimes, and aboveground operational and sovereign risks. Apache has found that implementing and sustaining a portfolio process requires technical solutions and application of best practices for three critical elements: Production Forecasting, Project Modeling & Economic Evaluation, and Portfolio Management & Decision Making. A robust portfolio process for investment decision-making requires organizational alignment around a shared vision for value recognition and a rigorous, disciplined approach to capital allocation. Value recognition is critically dependent on establishing internal practices and standards for consistent application of methods and tools in characterizing cash flow potential from the suite of investment opportunities. Apache’s implementation of a portfolio process was undertaken as a sequence of initiatives with clear deliverables focused on building critical capabilities and infrastructure within key groups, while driving organizational alignment around the process. Major steps in the change management effort included: creation of a portfolio modeling groupshifting focus from well characterizations to project characterizationssoftware and systems investmentsorganizational alignmentexecutive adoption Over the past decade, Apache has undertaken a major shift in strategic focus toward organic growth, by placing significant investments in North American unconventional resource plays. The worldwide portfolio of opportunities became increasingly complex in terms of demand for capital, pattern of cash flows, and uncertainty in outcomes. Comparing and contrasting the performance potential and limitations of the opportunity set, in the context of corporate goals and constraints, became increasingly difficult with standard ranking methodologies. Apache’s portfolio reached the enviable state of possessing more projects in inventory than capital available for funding. With each business unit (BU) focused on extending and optimizing its own opportunity set the overall, integrated value for the corporation was not fully recognized. A realignment of processes and a shift in cultural perspective emphasizing an integrated whole was needed.