In the last decade, the hydrocarbon production from shale plays has increased dramatically, which has greatly impacted the petroleum industry. While this increased production has benefited the industry, at the same time these unconventional resources have presented many challenges to oil and gas reserves evaluators. One of these challenges is predicting long-term shale production performance and life, especially in a timely and reliable manner. When Arps derived the hyperbolic model in 1944, b-factor was assumed as a constant and limited to values less than or equal to 1.0. However, many literature papers and field observations have shown that the b-factor instead changes with time in shale wells and, in many cases, can be well above 1.0, especially during the transient period. As a result, evaluators have modified the original DCA ("modified hyperbolic model") to incorporate a b-factor larger than 1.0 and a minimum exponential decline rate (Dmin) at the late-time life in shale production predictions. This paper further discusses this transient b-factor effect and the benefits of the Extended Exponential Decline Curve Analysis (EEDCA), (Zhanget. al , 2015). EEDCA does not require an estimate of when to switch to a Boundary-Dominated Flow (BDF) model or when to switch to exponential decline for shale oil and gas. All of the model parameters are interdependent and can be calibrated by fitting from the first production data point. Meanwhile, the Dmin used in the modified hyperbolic model is independent from early-time data. This paper further extends the use of EEDCA by applying it to conventional wells. It shows EEDCA is a useful alternative for the Arps method while it holds certain advantages in forecasting shale production. Thus, EEDCA is an integrated solution for both conventional and unconventional reservoirs.

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In 2011, the Society of Petroleum Evaluation Engineers (SPEE) published Monograph 3 as an industry guideline for reserves evaluation of unconventionals, especially for probabilistic approaches. This paper illustrates the workflow recommended by Monograph 3. The authors also point out some dilemmas one may encounter when applying the guidelines. Finally, the authors suggest remedies to mitigate limitations and improve the utility of the approach. This case study includes about 300 producing shale wells in the Permian Basin. Referring to Monograph 3, analogous wells were identified based on location, geology, drilling-and-completion (D&C) technology; Technically Recoverable Resources (TRRs) of these analogous wells were then evaluated by Decline Curve Analysis (DCA). Next, five type-wells were developed with different statistical characteristics. Lastly, a number of drilling opportunities were identified and, consequently, a Monte Carlo simulation was conducted to develop a statistical distribution for undeveloped locations in each type-well area. The authors demonstrated the use of probit plots and demonstrated the binning strategy, which could best represent the study area. The authors tuned the binning strategy based on multiple yardsticks, including median values of normalized TRRs per lateral length, slopes of the distribution lines in lognormal plots, ratios of P10 over P90, and well counts in each type-well category in addition to other variables. The binning trials were based on different geographic areas, producing reservoirs, and operators, and included the relatively new concept of a "learning curve" introduced by the Society of Petroleum Engineers (SPE) 2018 Petroleum Resources Management System (PRMS). To the best of the authors’ knowledge, this paper represents the first published case study to factor in the "learning curves" method. This paper automated the illustrated workflow through coded database queries or manipulation, which resulted in high efficiencies for multiple trials on binning strategy. The demonstrated case study illustrates valid decision-making processes based on data analytics. The case study further identifies methods to eliminate bias, and present independent objective reserves evaluations. Most of the challenges and situations herein are not fully addressed in Monograph 3 and are not documented in the regulations of the U.S. Security and Exchange Commission (SEC) or in the PRMS guidelines. While there may be differing approaches, and some analysts may prefer alternate methods, the authors believe that the items presented herein will benefit many who are starting to incorporate Monograph 3 in their work process. The authors hope that this paper will encourage additional discussion in our industry.

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