Hyperbolic Decline Parameters During and After Linear Flow: Field Example from the Barnett Shale Using Public Data

Pratikno, H.; Reese, D.E.; Summers, Laine E.

doi:10.2118/171568-ms

Cited by 4 publications

References 10 publications

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Machine Learning as a Reliable Technology for Evaluating Time-Rate Performance of Unconventional Wells

Fulford

Bowie

Berry

et al. 2015

Day 3 Wed, September 30, 2015

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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.

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Machine Learning as a Reliable Technology for Evaluating Time-Rate Performance of Unconventional Wells

Fulford

Bowie

Berry

et al. 2015

Day 3 Wed, September 30, 2015

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Machine Learning as a Reliable Technology for Evaluating Time/Rate Performance of Unconventional Wells

Fulford

Bowie

Berry

et al. 2016

SPE Economics &Amp; Management

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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.

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Shale Reserve Forecasting - Model Consistency and Uncertainty

Whitson¹,

Coll

Dahouk³

et al. 2016

All Days

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Three general categories of modeling are traditionally used to provide shale reserve forecasting -(1) decline curve analysis (DCA), (2) rate-time analysis (RTA), and (3) numerical model history matching (HM). The focus of this paper is aligning each of the three modeling approaches to ensure maximum consistency in terms of fundamental reservoir description, including (but not limited to) initial fluid in place, reservoir rock properties, PVT, well completion factors, fracture area and conductivity, well controls, and definition of infinite-acting and boundary-dominated flow regimes. The HM model approach, though more rigorous, is time consuming and cannot be used for the hundreds of wells in a typical shale field. We recommend, as have others, that history-matched numerical models be used to help calibrate RTA and DCA models in a consistent manner for all wells. Once a consistent model framework is achieved, reserve forecasting can be better understood by regulators, engineering and reserve teams within the operating company and their partners. Furthermore, a consistent modeling framework can provide more reliable uncertainty analysis to establish probabilistic reserves estimates in terms of P90-P50-P10 values (1P-2P-3P).Modeling methods used in forecasting shale reserves are based on production data that includes rates and pressures. DCA applies the boundary-dominated methods such as Arps, where multiple time regions are used to capture infinite-acting and boundary-dominated flow. RTA uses dimensionless pressure and rate solutions applicable to horizontal wells with multiple fractures, including superposition, pseudopressure and pseudotime. Numerical models solve the complex set of differential equations describing multiphase fluid flow using a properly-selected grid refinement (e.g. near fractures) and, in some cases, a dual porosity/dual permeability treatment of fracture-matrix interaction.

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Hyperbolic Decline Parameters During and After Linear Flow: Field Example from the Barnett Shale Using Public Data

Cited by 4 publications

References 10 publications

Machine Learning as a Reliable Technology for Evaluating Time-Rate Performance of Unconventional Wells

Machine Learning as a Reliable Technology for Evaluating Time-Rate Performance of Unconventional Wells

Machine Learning as a Reliable Technology for Evaluating Time/Rate Performance of Unconventional Wells

Shale Reserve Forecasting - Model Consistency and Uncertainty

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