Reservoir modeling of shale gas and tight oil presents numerous challenges due to complicated transport mechanisms and the existence of fracture networks. Even then, oil and gas companies have not slowed down on shale hydrocarbon investment and production using horizontal well drilling and hydraulic fracturing techniques. Many small oil companies may not have the budget to build a reservoir model which typically requires drilling test wells and performing well logging measurements. Even for large oil companies, building a reservoir model is not worthwhile for the evaluation of small-scale oil fields. Comprehensive numerical simulation methods are likely impractical in those cases. Decline Curve Analysis (DCA) is one of the most convenient and practical techniques in order to forecast the production of these reservoirs. With the rapid increase in shale hydrocarbon production over the past 30 years, there have been numerous production data for shale gas reservoirs. Many different DCA models have been constructed to model the shale hydrocarbon production rate, from the classical Arps to the latest and more advanced models; each has its advantages and shortcomings. In practice and in all existing commercial DCA software, most of these DCA models are implemented and open to be used. Most of the deterministic DCA models are empirical and lack a physical background so that they cannot be used for history-matching of the reservoir properties. In this study, popular DCA models for shale gas reservoirs are reviewed, including the types of reservoirs they fit. Their advantages and disadvantages have also been presented. This work will serve as a guideline for petroleum engineers to determine which DCA models should be applied to different shale hydrocarbon fields and production periods. The research objective also includes evaluating the performance of top unconventional plays (Bakken, Barnett, and Eagle Ford). Productions by counties are analyzed and compared to see how they stack up against each other. One section of this study also sheds some light on the future of shale gas and tight oil plays based on the simulation of models created.

Unconventional Reservoir Rate-Transient Analysis provides petroleum engineers and geoscientists with the first comprehensive review of rate-transient analysis (RTA) methods as applied to unconventional reservoirs. Volume One—Fundamentals, Analysis Methods, and Workflow is comprised of five chapters which address key concepts and analysis methods used in RTA. This volume overviews the fundamentals of RTA, as applied to low-permeability oil and gas reservoirs exhibiting simple reservoir and fluid characteristics. Volume Two—Application to Complex Reservoirs, Exploration and Development is comprised of four chapters that demonstrate how RTA can be applied to coalbed methane reservoirs, shale gas reservoirs, and low-permeability/shale reservoirs exhibiting complex behavior such as multiphase flow. Use of RTA to assist exploration and development programs in unconventional reservoirs is also demonstrated. This book will serve as a critical guide for students, academics, and industry professionals interested in applying RTA methods to unconventional reservoirs. Gain a comprehensive review of key concepts and analysis methods used in modern rate-transient analysis (RTA) as applied to low-permeability ("tight") oil and gas reservoirs Improve your RTA methods by providing reservoir/hydraulic fracture properties and hydrocarbon-in-place estimates for unconventional gas and light oil reservoirs exhibiting complex reservoir behaviors Understand the provision of a workflow for confident application of RTA to unconventional reservoirs

ne of the most important aspects in the life-cycle of a petroleum well is understanding, and being able to reasonably predict, the total hydrocarbon output that a well will have through its producing life. The estimation of reserves has strong economic and legal implications that will not only determine whether a well-drilling plan is viable but also the worth of a company itself. This study aims to better understand the two primary production forecasting methods used in the petroleum industry: decline curve analyses and numerical reservoir simulations, and their ability to complement one another to make a better-informed production forecast when used together. Decline curve analyses have a heavy reliance on prior hydrocarbon production data which presents difficulty in forecasting during early-term behavior due to a lack of production data; however, reservoir simulations are stronger in early well-life because they are based more heavily on reservoir parameters. The objective is to use reservoir simulations to inform the decline curve's early time behavior, by informing parameters in their equations such as Dmin, while developing a correlational relationship between the two forecasting techniques that could be applied and translated to other reservoirs in the future. A decline curve analysis was performed on a three-well study area and Dmin values of 6%, 8%, and 10% were evaluated. The matching process of the decline curves heavily relied on the cumulative production in addition to the production rates, which used a thirty-day rolling average of the daily production data. Two equivalent numerical reservoir simulation models were built for the Eagle Ford which primarily used literature sourced values for the properties. The models were history matched to the observed data very well, though each model indicated different conclusions for a suggested Dmin value. Further compounding the results, the range of uncertainty in the matrix porosity property is larger than the range of the Dmin values. Due to this, the authors are not able to use the simulation models to inform parameters in decline curve analyses nor attempt to translate that relationship to other reservoirs.

Material balance is an essential reservoir engineering tool that is used to determine original hydrocarbon in place and the production performance of a reservoir. There are several types of material-balance approaches developed, each with its own application. Such approaches include integral material balance, differential material balance and flowing material balance. In this thesis, a form of differential material balance, similar to the one developed by Muskat for Solution Gas Drive Reservoirs, has been derived for unconventional gas-water reservoirs impacted by adsorption. Originally, the developed Muskat-type equation is in the pressure domain, but it can also be derived in other domains such as the time domain and the cumulative produced fluids domains. The resulting system of ordinary differential equations (ODEs) are then solved using fourth order Runge-Kutta method which is a traditional ODE solver. The system of two differential equations for the Muskat-type equation in the time domain (time as an independent variable), are formulated with pressure and water saturation as the dependent variables. These resulting ODEs are then used to forecast and analyze the production profiles of a gas-water reservoir considering adsorption. The semi-analytical model is then validated internally using finite difference and analytical rate derivative equations, and externally by benchmarking it with a numerical simulator. The significant factor that caused the disparity between the semi-analytical model proposed in this study and the numerical simulator is the time it takes to reach pseudo-steady state flow (t_pss) with lower times producing better results. At t_pss less than 0.111 days , numerical simulation is almost replaceable in forecasting rates. However, at t_pss less than 0.717 days, cumulative gas produced can be accurately forecasted. This is to be expected and a reservoir simulator is fully transient, while material balance is based on the pseudo steady-state flow regime. This study provided a unique opportunity to investigate the characteristics of the production profile such as the peak rate and the observed inflection points while also identifying the reservoir parameters that affect them. Moreover, an equation has been developed that can be used to identify and describe the peak rate. This equation makes use of the byproduct of the Muskat-type equation ((dS_w)/dP) which can be modified in terms of rock and fluid properties to aid in history matching. Furthermore, three well specifications were investigated (constant well pressure, constant drawdown, and constant water production rate) with only two of the three producing a peak rate no peak gas production rate was observed for water rate specified wells. This study also showed that material balance can be used to replace decline curve analysis under certain conditions. This is mainly due to the reduced time to pseudo-steady state (t_pss) caused by the low total compressibility (rock and water), high permeability, low water viscosity, and low drainage area. At a threshold of t_pss less than 0.178 days, an accurate late-time forecast can be attained.Since the proposed semi-analytical model provided water saturation values for different pressures, a non-iterative methodology has been developed to improve upon Kings (G. R. King, 1993) iterative integral material balance equation for unconventional reservoirs.Through this study, a number of significant observations were made. It was found that at a low rock compressibility, the change in saturation over time can be estimated using the water production profile and initial porosity and water formation volume factor. Also, the saturation of gas can be estimated using the percentage of water produced from the original water in place (OWIP), adjusted for desorption time, at an increasing accuracy as the rock compressibility is decreased. Additionally, the cause of a phenomenon known as dual peaking which occurs in field and simulation data of CBM reservoirs has been identified to be due to the transient-state production.

As unconventional reservoir activity grows in demand, reservoir engineers relying on history matching are challenged with this time-consuming task in order to characterize hydraulic fracture and reservoir properties, which are expensive and difficult to obtain. Assisted History Matching for Unconventional Reservoirs delivers a critical tool for today's engineers proposing an Assisted History Matching (AHM) workflow. The AHM workflow has benefits of quantifying uncertainty without bias or being trapped in any local minima and this reference helps the engineer integrate an efficient and non-intrusive model for fractures that work with any commercial simulator. Additional benefits include various applications of field case studies such as the Marcellus shale play and visuals on the advantages and disadvantages of alternative models. Rounding out with additional references for deeper learning, Assisted History Matching for Unconventional Reservoirs gives reservoir engineers a holistic view on how to model today's fractures and unconventional reservoirs. Provides understanding on simulations for hydraulic fractures, natural fractures, and shale reservoirs using embedded discrete fracture model (EDFM) Reviews automatic and assisted history matching algorithms including visuals on advantages and limitations of each model Captures data on uncertainties of fractures and reservoir properties for better probabilistic production forecasting and well placement

Provides comprehensive information about the key exploration, development and optimization concepts required for gas shale reservoirs Includes statistics about gas shale resources and countries that have shale gas potential Addresses the challenges that oil and gas industries may confront for gas shale reservoir exploration and development Introduces petrophysical analysis, rock physics, geomechanics and passive seismic methods for gas shale plays Details shale gas environmental issues and challenges, economic consideration for gas shale reservoirs Includes case studies of major producing gas shale formations

This work presents the various probabilistic methodology for forecasting petroleum production in shale reservoirs. Two statistical methods are investigated, Bayesian and frequentist, combined with various decline curve deterministic models. A robust analysis of well-completion properties and how they affect the production forecast is carried out. Lastly, a look into the uncertainties introduced by the statistical methods and the decline curve models are investigated to discover any correlation and plays that otherwise would not be apparent. We investigated two Bayesian methods - Absolute Bayesian Computation (ABC) and GIBBS sampler - and two frequentist methods - Conventional Bootstrap (BS) and Modified Bootstrap (MBS). We combined these statistical methods with five empirical models - Arps, Duong, Power Law Model (PLE), Logistic Growth Model (LGA), and Stretched Exponential Decline Model (SEPD) - and an analytical Jacobi 2 theta model. This allowed us to make a robust comparison of all these approaches on various unconventional plays across the United States, including Permian, Marcellus, Eagle Ford, Haynesville, Barnett, and Bakken shale, to get detailed insight on how to forecast production with minimal prediction errors effectively. Analysis was carried out on a total of 1800 wells with varying production history data lengths ranging from 12 to 60 months on a 12-month increment and a total production length of 96 months. We developed a novel approach for developing and integrating informative model parameter priors into the Bayesian statistical methods. Previous work assumed a uniform distribution for model parameter priors, which was inaccurate and negatively impacted forecasting performance. Our results show the significant superior performance of the Bayesian methods, most notably at early hindcast size (12 to 24 months production history). Furthermore, we discovered that production history length was the most critical factor in production forecasting that leveled the performance of all probabilistic methods regardless of the decline curve model or statistical methodology implemented. The novelty of this work relies on the development of informative priors for the Bayesian methodologies and the robust combination of statistical methods and model combination studied on a wide variety of shale plays. In addition, the whole methodology was automated in a programming language and can be easily reproduced and used to make production forecasts accurately.