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Comparison Of Various Deterministic Forecasting Techniques In Shale Gas Reservoirs With Emphasis On The Duong Method
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Book Synopsis Comparison of Various Deterministic Forecasting Techniques in Shale Gas Reservoirs with Emphasis on the Duong Method by : Krunal Jaykant Joshi
Download or read book Comparison of Various Deterministic Forecasting Techniques in Shale Gas Reservoirs with Emphasis on the Duong Method written by Krunal Jaykant Joshi and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: There is a huge demand in the industry to forecast production in shale gas reservoirs accurately. There are many methods including volumetric, Decline Curve Analysis (DCA), analytical simulation and numerical simulation. Each one of these methods has its advantages and disadvantages, but only the DCA technique can use readily available production data to forecast rapidly and to an extent accurately. The DCA methods in use in the industry such as the Arps method had originally been developed for Boundary dominated flow (BDF) wells but it has been observed in shale reservoirs the predominant flow regime is transient flow. Therefore it was imperative to develop newer models to match and forecast transient flow regimes. The SEDM/SEPD, the Duong model and the Arps with a minimum decline rate are models that have the ability to match and forecast wells with transient flow followed by boundary flow. I have revised the Duong model to forecast better than the original model. I have also observed a certain variation of the Duong model proves to be a robust model for most of the well cases and flow regimes. The modified Duong has been shown to work best compared to other deterministic models in most cases. For grouped datasets the SPED & Duong models forecast accurately while the Modified Arps does a poor job.
Book Synopsis Evaluation of the Stretched Exponential Production Decline Model and Comparison to Other Decline Models for Shale Reservoirs by : Dong Li
Download or read book Evaluation of the Stretched Exponential Production Decline Model and Comparison to Other Decline Models for Shale Reservoirs written by Dong Li and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The discovery and development of shale oil/gas has changed the energy industry. By 2040, shale gas production will account for 50% of the total natural gas production of the U.S. Due to the extremely low permeability of shale reservoirs, shale gas wells exhibit much longer transient flow periods than conventional wells, and this makes it inappropriate to use conventional methods of evaluating estimated ultimate recovery (EUR) of wells in these reservoirs. Therefore, new methods of forecasting shale wells are needed. In this study, I focused on the stretched exponential production decline model (SEPD), and particularly Yu’s modification of the model (YM-SEPD). I compared the results with other methods, including Duong’s method, and the Arps hyperbolic model. SEPD provided the most reliable EURs for shale gas well when excluding early off-trend data. YM-SEPD gave results comparable to SEPD and is much easier to apply. It is therefore the method we recommend for shale wells.
Book Synopsis Application of Probabilistic Decline Curve Analysis to Unconventional Reservoirs by : Uchenna C. Egbe
Download or read book Application of Probabilistic Decline Curve Analysis to Unconventional Reservoirs written by Uchenna C. Egbe and published by . This book was released on 2022 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Stretched Exponential Decline Model as a Probabilistic and Deterministic Tool for Production Forecasting and Reserve Estimation in Oil and Gas Shales by : Babak Akbarnejad Nesheli
Download or read book Stretched Exponential Decline Model as a Probabilistic and Deterministic Tool for Production Forecasting and Reserve Estimation in Oil and Gas Shales written by Babak Akbarnejad Nesheli and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Today everyone seems to agree that ultra-low permeability and shale reservoirs have become the potentials to transform North America's oil and gas industry to a new phase. Unfortunately, transient flow is of long duration (perhaps life of the well) in ultra-low permeability reservoirs, and traditional decline curve analysis (DCA) models can lead to significantly over-optimistic production forecasts without additional safeguards. Stretched Exponential decline model (SEDM) gives considerably more stabilized production forecast than traditional DCA models and in this work it is shown that it produces unchanging EUR forecasts after only two-three years of production data are available in selected reservoirs, notably the Barnett Shale. For an individual well, the SEDM model parameters, can be determined by the method of least squares in various ways, but the inherent nonlinear character of the least squares problem cannot be bypassed. To assure a unique solution to the parameter estimation problem, this work suggests a physics-based regularization approach, based on critical velocity concept. Applied to selected Barnett Shale gas wells, the suggested method leads to reliable and consistent EURs. To further understand the interaction of the different fracture properties on reservoir response and production decline curve behavior, a series of Discrete Fracture Network (DFN) simulations were performed. Results show that at least a 3-layer model is required to reproduce the decline behavior as captured in the published SEDM parameters for Barnett Shale. Further, DFN modeling implies a large number of parameters like fracture density and fracture length are in such a way that their effect can be compensated by the other one. The results of DFN modeling of several Barnett Shale horizontal wells, with numerous fracture stages, showed a very good agreement with the estimated SEDM model for the same wells. Estimation of P90 reserves that meet SEC criteria is required by law for all companies that raise capital in the United States. Estimation of P50 and P10 reserves that meet SPE/WPC/AAPG/SPEE Petroleum Resources Management System (PRMS) criteria is important for internal resource inventories for most companies. In this work a systematic methodology was developed to quantify the range of uncertainty in production forecast using SEDM. This methodology can be used as a probabilistic tool to quantify P90, P50, and P10 reserves and hence might provide one possible way to satisfy the various legal and technical-society-suggested criteria.
Book Synopsis Challenges in Modelling and Simulation of Shale Gas Reservoirs by : Jebraeel Gholinezhad
Download or read book Challenges in Modelling and Simulation of Shale Gas Reservoirs written by Jebraeel Gholinezhad and published by Springer. This book was released on 2017-12-27 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the problems involved in the modelling and simulation of shale gas reservoirs, and details recent advances in the field. It discusses various modelling and simulation challenges, such as the complexity of fracture networks, adsorption phenomena, non-Darcy flow, and natural fracture networks, presenting the latest findings in these areas. It also discusses the difficulties of developing shale gas models, and compares analytical modelling and numerical simulations of shale gas reservoirs with those of conventional reservoirs. Offering a comprehensive review of the state-of-the-art in developing shale gas models and simulators in the upstream oil industry, it allows readers to gain a better understanding of these reservoirs and encourages more systematic research on efficient exploitation of shale gas plays. It is a valuable resource for researchers interested in the modelling of unconventional reservoirs and graduate students studying reservoir engineering. It is also of interest to practising reservoir and production engineers.
Book Synopsis Comparison of Single, Double, and Triple Linear Flow Models for Shale Gas/oil Reservoirs by : Vartit Tivayanonda
Download or read book Comparison of Single, Double, and Triple Linear Flow Models for Shale Gas/oil Reservoirs written by Vartit Tivayanonda and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: There have been many attempts to use mathematical method in order to characterize shale gas/oil reservoirs with multi-transverse hydraulic fractures horizontal well. Many authors have tried to come up with a suitable and practical mathematical model. To analyze the production data of a shale reservoir correctly, an understanding and choosing the proper mathematical model is required. Therefore, three models (the homogeneous linear flow model, the transient linear dual porosity model, and the fully transient linear triple porosity model) will be studied and compared to provide correct interpretation guidelines for these models. The analytical solutions and interpretation guidelines are developed in this work to interpret the production data of shale reservoirs effectively. Verification and derivation of asymptotic and associated equations from the Laplace space for dual porosity and triple porosity models are performed in order to generate analysis equations. Theories and practical applications of the three models (the homogeneous linear flow model, the dual porosity model, and the triple porosity model) are presented. A simplified triple porosity model with practical analytical solutions is proposed in order to reduce its complexity. This research provides the interpretation guidelines with various analysis equations for different flow periods or different physical properties. From theoretical and field examples of interpretation, the possible errors are presented. Finally, the three models are compared in a production analysis with the assumption of infinite conductivity of hydraulic fractures.
Book Synopsis A New Method for History Matching and Forecasting Shale Gas/oil Reservoir Production Performance with Dual and Triple Porosity Models by : Orkhan Samandarli
Download or read book A New Method for History Matching and Forecasting Shale Gas/oil Reservoir Production Performance with Dual and Triple Porosity Models written by Orkhan Samandarli and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Different methods have been proposed for history matching production of shale gas/oil wells which are drilled horizontally and usually hydraulically fractured with multiple stages. These methods are simulation, analytical models, and empirical equations. It has been well known that among the methods listed above, analytical models are more favorable in application to field data for two reasons. First, analytical solutions are faster than simulation, and second, they are more rigorous than empirical equations. Production behavior of horizontally drilled shale gas/oil wells has never been completely matched with the models which are described in this thesis. For shale gas wells, correction due to adsorption is explained with derived equations. The algorithm which is used for history matching and forecasting is explained in detail with a computer program as an implementation of it that is written in Excel's VBA. As an objective of this research, robust method is presented with a computer program which is applied to field data. The method presented in this thesis is applied to analyze the production performance of gas wells from Barnett, Woodford, and Fayetteville shales. It is shown that the method works well to understand reservoir description and predict future performance of shale gas wells. Moreover, synthetic shale oil well also was used to validate application of the method to oil wells. Given the huge unconventional resource potential and increasing energy demand in the world, the method described in this thesis will be the "game changing" technology to understand the reservoir properties and make future predictions in short period of time.
Book Synopsis Production Analysis and Forecasting of Shale Reservoirs Using Simple Mechanistic and Statistical Modeling by : Leopoldo Matias Ruiz Maraggi
Download or read book Production Analysis and Forecasting of Shale Reservoirs Using Simple Mechanistic and Statistical Modeling written by Leopoldo Matias Ruiz Maraggi and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate production analysis and forecasting of well’s performance is essential to estimate reserves and to develop strategies to optimize hydrocarbon recovery. In the case of shale resources, this task is particularly challenging for the following reasons. First, these reservoirs show long periods of transient linear flow in which the reservoir volume grows continuously over time acting without bounds. Second, variable operating conditions cause scatter and abrupt production changes. Finally, the presence of competing flow mechanisms, heterogeneities, and multi-phase flow effects make the production analysis more complex. Detailed numerical flow models can address the complexities present in unconventional reservoirs. However, these models suffer from the following limitations: (a) the uncertainty of many input parameters, (b) susceptibility to overfit the data, (c) lack of interpretability of their results, and (d) high computational expense. This dissertation provides new and simple mechanistic and statistical modeling tools suitable to improve the production analysis and forecasts of shale reservoirs. This work presents solutions to the following research problems. This study develops and applies a new two-phase (oil and gas) flow suitable to history-match and forecast production of tight-oil and gas-condensate reservoirs producing below bubble- and dew-point conditions, respectively. It solves flow equations in dimensionless form and uses only two scaling parameters (hydrocarbon in-place and characteristic time) to history-match production. For this reason, the model requires minimal time to run making it ideal for decline curve analysis on large numbers of wells. This research illustrates the development and application of a Bayesian framework that generates probabilistic production history matches and forecasts to address the uncertainty of model’s estimates. This work uses an adaptative Metropolis-Hastings Markov chain Monte Carlo (MCMC) algorithm to guarantee a fast convergence of the Markov chains by accounting for the correlation among model’s parameters. In addition, this study calibrates the model’s probabilistic estimates using hindcasting and evaluates the inferences robustness using posterior predictive checks. This dissertation examines the problem of evaluation, ranking and selection, and averaging of models for improved probabilistic production history-matching and forecasting. We illustrate the assessment of the predictive accuracy of four rate-time models using the expected log predictive density (elpd) accuracy metric along with cross-validation techniques (leave-one-out and leave-future-out). The elpd metric provides a measure of out-of-sample predictive accuracy of a model’s posterior distribution. The application of Pareto smoothed importance sampling (PSIS) allows to use cross-validation techniques without the need of refitting Bayesian models. Using the Bayesian Bootstrap, this work generates a model ensemble that weighs each individual model based on the accuracy of its predictions. Finally, this research applies a novel deconvolution technique to incorporate changing operating conditions into rate-time analysis of tight-oil and shale gas reservoirs. Furthermore, this work quantifies the errors and discusses the limitations of the standard rate-transient analysis technique used in production analysis of unconventional reservoirs: rate normalization and material balance time
Book Synopsis Using Decline Curve Analysis, Volumetric Analysis, and Bayesian Methodology to Quantify Uncertainty in Shale Gas Reserve Estimates by : Raul Alberto Gonzalez Jimenez
Download or read book Using Decline Curve Analysis, Volumetric Analysis, and Bayesian Methodology to Quantify Uncertainty in Shale Gas Reserve Estimates written by Raul Alberto Gonzalez Jimenez and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic decline curve analysis (PDCA) methods have been developed to quantify uncertainty in production forecasts and reserves estimates. However, the application of PDCA in shale gas reservoirs is relatively new. Limited work has been done on the performance of PDCA methods when the available production data are limited. In addition, PDCA methods have often been coupled with Arp's equations, which might not be the optimum decline curve analysis model (DCA) to use, as new DCA models for shale reservoirs have been developed. Also, decline curve methods are based on production data only and do not by themselves incorporate other types of information, such as volumetric data. My research objective was to integrate volumetric information with PDCA methods and DCA models to reliably quantify the uncertainty in production forecasts from hydraulically fractured horizontal shale gas wells, regardless of the stage of depletion. In this work, hindcasts of multiple DCA models coupled to different probabilistic methods were performed to determine the reliability of the probabilistic DCA methods. In a hindcast, only a portion of the historical data is matched; predictions are made for the remainder of the historical period and compared to the actual historical production. Most of the DCA models were well calibrated visually when used with an appropriate probabilistic method, regardless of the amount of production data available to match. Volumetric assessments, used as prior information, were incorporated to further enhance the calibration of production forecasts and reserves estimates when using the Markov Chain Monte Carlo (MCMC) as the PDCA method and the logistic growth DCA model. The proposed combination of the MCMC PDCA method, the logistic growth DCA model, and use of volumetric data provides an integrated procedure to reliably quantify the uncertainty in production forecasts and reserves estimates in shale gas reservoirs. Reliable quantification of uncertainty should yield more reliable expected values of reserves estimates, as well as more reliable assessment of upside and downside potential. This can be particularly valuable early in the development of a play, because decisions regarding continued development are based to a large degree on production forecasts and reserves estimates for early wells in the play. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/148436
Book Synopsis Production Forecasting in Shale Volatile Oil Reservoirs by : Ibukun Makinde
Download or read book Production Forecasting in Shale Volatile Oil Reservoirs written by Ibukun Makinde and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis gives us a better understanding of the behavior of shale volatile oil reservoirs. The effects of fluid compositions as well as the sensitivity of certain variables on cumulative oil production and rates were analyzed using black-oil and compositional simulations. Two-phase (oil and gas) black-oil simulations gave better results than single-phase (oil) black-oil simulations. Compositional simulations were much better in comparison to two-phase black-oil simulations. Therefore, for thorough analysis of fluid composition effects and more accurate production forecasts (especially for reservoir fluids like volatile oils in shale formations), compositional simulations are necessary. In this research, single-phase and two-phase black-oil simulations were run on a base case model and the results were compared. Sensitivity studies were carried out by varying certain parameters in the base case model, then single-phase and two-phase black-oil simulations were run and the results were compared to the base case model. This was followed by analyzing six different fluid samples through compositional simulations. Flash calculations were later done on the fluid samples to obtain inputs for two-phase black-oil simulations. Finally, the simulation results from the compositional and two-phase black-oil simulations were then compared. The importance of shale oil and gas research cannot be over-emphasized, given the ever-rising global demand for energy. Research and studies like this, can lead to better well completions and design, improve reservoir management and economics as well as provide insight into potential alternative methods to enhance recovery from unconventional shale formations.
Book Synopsis Uncertainty Quantification in Unconventional Reservoirs Using Conventional Bootstrap and Modified Bootstrap Methodology by : Chukwuemeka Okoli
Download or read book Uncertainty Quantification in Unconventional Reservoirs Using Conventional Bootstrap and Modified Bootstrap Methodology written by Chukwuemeka Okoli and published by . This book was released on 2020 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: Various uncertainty quantication methodologies are presented using a combination of several deterministic decline curve analysis models and two bootstrapping algorithms. The bootstrapping algorithms are the conventional bootstrapping method (CBM) and the modied bootstrapping method (MBM). The combined deterministic-stochastic combination models are applied to 126 sample wells from the Permian basin. Results are presented for 12 to 72 months of production hindcast given an average well production history of 120 months. Previous researchers used the Arps model and both conventional and modied bootstrapping with block re-sampling techniques to reliably quantify uncertainty in production forecasts. In this work, we applied both stochastic techniques to other decline curve analysis models|namely, the Duong and the Stretched Exponential Production Decline (SEPD) models. The algorithms were applied to sample wells spread across the three main sub-basins of the Permian. A description of how both the deterministic and stochastic methods can be combined is provided. Also, pseudo-codes that describes the methodologies applied in this work is provided to permit readers to replicate results if necessary. Based on the average forecast error plot in the Permian Basin for 126 active wells, we can also conclude that the MBM-Arps, CBM-Arps, and MBM-SEPD combinations produce P50 forecasts that match cumulative production best regardless of the sub-basin and amount of production hindcast used. Regardless of concerns about the coverage rate, the CBM-Arps, MBM-Arps, CBM-SEPD, and MBMSEPD algorithm combinations produce cumulative P50 predictions within 20% of the true cumulative production value using only a 24-month hindcast. With a 12 month-hindcast, the MBM-Arps combined model produced cumulative P50 predictions with a forecast error of approximately 20%. Also, the CBM-SEPD and MBM-SEPD models were within 30% of the true cumulative production using a 12- month hindcast. Another important result is that all the deterministic-stochastic method combinations studied under-predicted the true cumulative production to varying degrees. However, the CBM-Duong combination was found to severely under-predict cumulative production, especially for the 12-month hindcast. It is not a suitable model combination based on forecast error, especially when hindcast fractions on the low end of the spectrum are used. Accordingly, the CBM- Duong combination is not recommended, especially if production history of no more than 24 months is available for hindcasting. As expected, the coverage rate increased, and the forecast error decreased for all the algorithm combinations with increasing hindcast duration. The novelty of this work lies in its extension of the bootstrapping technique to other decline curve analysis models. The software developed can also be used to analyze many wells quickly on a standard engineering computer. This research is also important because realistic estimates of reserves can be estimated in plays like the Permian basin when uncertainty is correctly quantied.
Book Synopsis Deterministic and Stochastic Analyses to Quantify the Reliability of Uncertainty Estimates in Production Decline Modeling of Shale Gas Reservoirs by : Brent L. Johanson
Download or read book Deterministic and Stochastic Analyses to Quantify the Reliability of Uncertainty Estimates in Production Decline Modeling of Shale Gas Reservoirs written by Brent L. Johanson and published by . This book was released on 2013 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Development of New Decline Model for Shale Oil Reserves by : Samit Shah
Download or read book Development of New Decline Model for Shale Oil Reserves written by Samit Shah and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis provides a new methodology to forecast ultimate recovery, based on more reliable production forecast for shale oil wells using historical production data. Compared to available decline curve methods including Arps (AIME: 160, 228-247), Valko (SPE 134231) and Duong (SPE 137748), this method is more accurate and more conservative. Production forecasts play a vital role in determining the value of oil or gas wells, and improved accuracy enhances management decisions on field development. The new, more accurate method was verified using both field data and numerical simulations. This method can potentially be used in most shale reservoirs producing single-phase liquid.
Book Synopsis Application of the Stretched Exponential Production Decline Model to Forecast Production in Shale Gas Reservoirs by : James Cody Statton
Download or read book Application of the Stretched Exponential Production Decline Model to Forecast Production in Shale Gas Reservoirs written by James Cody Statton and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Production forecasting in shale (ultra-low permeability) gas reservoirs is of great interest due to the advent of multi-stage fracturing and horizontal drilling. The well renowned production forecasting model, Arps' Hyperbolic Decline Model, is widely used in industry to forecast shale gas wells. Left unconstrained, the model often overestimates reserves by a great deal. A minimum decline rate is imposed to prevent overestimation of reserves but with less than ten years of production history available to analyze, an accurate minimum decline rate is currently unknown; an educated guess of 5% minimum decline is often imposed. Other decline curve models have been proposed with the theoretical advantage of being able to match linear flow followed by a transition to boundary dominated flow. This thesis investigates the applicability of the Stretched Exponential Production Decline Model (SEPD) and compares it to the industry standard, Arps' with a minimum decline rate. When possible, we investigate an SEPD type curve. Simulated data is analyzed to show advantages of the SEPD model and provide a comparison to Arps' model with an imposed minimum decline rate of 5% where the full production history is known. Long-term production behavior is provided by an analytical solution for a homogenous reservoir with homogenous hydraulic fractures. Various simulations from short-term linear flow (~1 year) to long-term linear flow (~20 years) show the ability of the models to handle onset of boundary dominated flow at various times during production history. SEPD provides more accurate reserves estimates when linear flow ends at 5 years or earlier. Both models provide sufficient reserves estimates for longer-term linear flow scenarios. Barnett Shale production data demonstrates the ability of the models to forecast field data. Denton and Tarrant County wells are analyzed as groups and individually. SEPD type curves generated with 2004 well groups provide forecasts for wells drilled in subsequent years. This study suggests a type curve is most useful when 24 months or less is available to forecast. The SEPD model generally provides more conservative forecasts and EUR estimates than Arps' model with a minimum decline rate of 5%.
Book Synopsis A Novel Approach For the Simulation of Multiple Flow Mechanisms and Porosities in Shale Gas Reservoirs by : Bicheng Yan
Download or read book A Novel Approach For the Simulation of Multiple Flow Mechanisms and Porosities in Shale Gas Reservoirs written by Bicheng Yan and published by . This book was released on 2013 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: The state of the art of modeling fluid flow in shale gas reservoirs is dominated by dual porosity models that divide the reservoirs into matrix blocks that significantly contribute to fluid storage and fracture networks which principally control flow capacity. However, recent extensive microscopic studies reveal that there exist massive micro- and nano- pore systems in shale matrices. Because of this, the actual flow mechanisms in shale reservoirs are considerably more complex than can be simulated by the conventional dual porosity models and Darcy's Law. Therefore, a model capturing multiple pore scales and flow can provide a better understanding of complex flow mechanisms occurring in these reservoirs. Through the use of a unique simulator, this research work establishes a micro-scale multiple-porosity model for fluid flow in shale reservoirs by capturing the dynamics occurring in three separate porosity systems: organic matter (mainly kerogen); inorganic matter; and natural fractures. Inorganic and organic portions of shale matrix are treated as sub-blocks with different attributes, such as wettability and pore structures. In the organic matter or kerogen, gas desorption and diffusion are the dominant physics. Since the flow regimes are sensitive to pore size, the effects of smaller pores (mainly nanopores and picopores) and larger pores (mainly micropores and nanopores) in kerogen are incorporated in the simulator. The separate inorganic sub-blocks mainly contribute to the ability to better model dynamic water behavior. The multiple porosity model is built upon a unique tool for simulating general multiple porosity systems in which several porosity systems may be tied to each other through arbitrary transfer functions and connectivities. This new model will allow us to better understand complex flow mechanisms and in turn to extend simulation to the reservoir scale including hydraulic fractures through upscaling techniques. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/151163
Book Synopsis Using Rate Transient Analysis and Bayesian Algorithms for Reservoir Characterization in Hydraulically Fractured Horizontal Gas Wells During Linear Flow by : Pirayu Yuhun
Download or read book Using Rate Transient Analysis and Bayesian Algorithms for Reservoir Characterization in Hydraulically Fractured Horizontal Gas Wells During Linear Flow written by Pirayu Yuhun and published by . This book was released on 2019 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-stage hydraulically fractured horizontal wells (MFHWs) are currently a popular method of developing shale gas and oil reservoirs. The performance of MFHWs can be analyzed by an approach called Rate transient analysis (RTA). However, the predicted outcomes are often inaccurate and provide non-unique results. Therefore, the main objective of this thesis is to couple Bayesian Algorithms with a current production analysis method, that is, rate transient analysis, to generate probabilistic credible interval ranges for key reservoir and completion variables. To show the legitimacy of the RTA-Bayesian method, synthetic production data from a multistage hydraulically fractured horizontal completion in a reservoir modeled after Marcellus shale reservoir was generated using a reservoir (CMG) model. The synthetic production data was analyzed using a combination of rate transient analysis with Bayesian techniques. Firstly, the traditional log-log plot was produced to identify the linear flow production regime, which is usually the dominant regime in shale reservoirs. Using the linear flow production data and traditional rate transient analysis equations, Bayesian inversion was carried out using likelihood-based and likelihood-free Bayesian methods. The rjags and EasyABC packages in statistical software R were used for the likelihood-based and likelihood-free inversion respectively. Model priors were based (1) on information available about the Marcellus shale from technical literature and (2) hydraulic fracture design parameters. Posterior distributions and prediction intervals were developed for the fracture length, matrix permeability, and skin factor. These predicted credible intervals were then compared with actual synthetic reservoir and hydraulic fracture data. The methodology was also repeated for an actual case in the Barnett shale for a validation. The most substantial finding was that for all the investigated cases, including complicated scenarios (such as finite fracture conductivity, fracturing fluid flowback, heterogeneity of fracture length, and pressure-dependent reservoir), the combined RTA-Bayesian model provided a reasonable prediction interval that encompassed the actual/observed values of the reservoir/hydraulic fracture variables. The R-squared value of predicted values over true values was more than 0.5 in all cases. For the base case in this study, the choice of the prior distribution did not affect the posterior distribution/prediction interval in a significant manner in as much as the prior distribution was partially informative. However, the use of noninformative priors resulted in a loss of precision. Also, a comparison of the Approximate Bayesian Computation (ABC) and the traditional Bayesian algorithms showed that the ABC algorithm reduced computational time with minimal loss of accuracy by at least an order of magnitude by bypassing the complicated step of having to compute the likelihood function. In addition, the production time, number of iterations and tolerance of fitting had a minimal impact on the posterior distribution after an optimum point--which was at least one-year production, 10,000 iterations and 0.001 respectively. In summary, the RTA-Bayesian production analysis method implemented in relatively easy computational platforms, like R and Excel, provided good characterization of all key variables such as matrix permeability, fracture length and skin when compared to results obtained from analytical methods. This probabilistic characterization has the potential to enable better understanding of well performance, improved identification of optimization opportunities and ultimately improved ultimate recovery from shale gas resources.
Book Synopsis Novel Probabilistic-based Framework for Improved History Matching of Shale Gas Reservoirs by : Francis Nzubechukwu Nwabia
Download or read book Novel Probabilistic-based Framework for Improved History Matching of Shale Gas Reservoirs written by Francis Nzubechukwu Nwabia and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hydraulically fractured horizontal wells are widely adopted for the development of tight or shale gas reservoirs. The presence of highly heterogeneous, multi-scale, fracture systems often renders any detailed characterization of the fracture properties challenging. The discrete fracture network (DFN) model offers a viable alternative for explicit representation of multiple fractures in the domain, where the comprising fracture properties are defined in accordance with specific probability distributions. However, even with the successful modelling of a DFN, the relationship between a set of fracture parameters and the corresponding production performance is highly nonlinear, implying that a robust history-matching workflow capable of updating the pertinent DFN model parameters is required for calibrating stochastic reservoir models to both geologic and dynamic production data. This thesis will develop an integrated approach for the history matching of hydraulically fractured reservoirs. First, multiple realizations of the DFN model are constructed with conditioning data based on available geological information such as seismic data, well logs, and rate transient analysis (RTA) interpretations, which are useful for inferring the prior probability distributions of relevant fracture parameters. A pilot point scheme and sequential indicator simulation are employed to update the distributions of fracture intensities which represent the abundance of secondary fractures (NFs) in the entire reservoir volume. Next, the model realizations are upscaled into an equivalent continuum dual-porosity dual-permeability model and subjected to numerical multiphase flow simulation. The predicted production performance is compared with the actual recorded responses. Finally, the DFN-model parameters are adjusted following an indicator-based probability perturbation method. Although the probability perturbation technique has been applied to update facies distributions in the past, its application in modeling DFN distributions is limited. An indicator formulation is proposed to account for the non-Gaussian nature of the DFN parameters. The algorithm aims at minimizing the objective function while reducing the uncertainties in the unknown fracture parameters. The novel probabilistic-based framework is applied to estimate the posterior probability distributions of transmissivity of the primary fracture (Tpf), transmissivity of the secondary induced fracture (Tsf) and secondary fracture intensity (Psf32L), secondary fracture aperture (re), length and height (L and H), in a multifractured shale gas well in the Horn River Basin. An initial realization of the DFN model is sampled from the prior probability distributions using the Monte Carlo simulation. These probability distributions are updated to match the production history, and multiple realizations of the DFN models are sampled from the updated (posterior) distributions accordingly. The key novelty in the developed probabilistic approach is that it accounts for the highly nonlinear relationships between fracture model parameters and the corresponding flow responses, and it yields an ensemble of DFN realizations calibrated to both static and dynamic data, as well as the related upscaled flow-simulation models. The results demonstrate the utility of the developed approach for estimating secondary fracture parameters, which are not inferable from other static information alone.
