Analysis of well performance data can deliver decision-making solutions regarding field development, production optimization, and reserves evaluation. Well performance analysis involves the study of the measured response of a system, the reservoir in our case, in the form of production rates and flowing pressures. The Eagle Ford shale in South Texas is one of the most prolific shale plays in the United States. However, the ultra-low permeability of the shale combined with its limited production history makes predicting ultimate recovery very difficult, especially in the early life of a well. Use of Rate Transient Analysis makes the analysis of early production data possible, which involves solving an inverse problem. Unlike the traditional decline analysis, Rate Transient Analysis requires measured production rates and flowing pressures, which were provided by an operator based in the Eagle Ford. This study is divided into two objectives. The first objective is to analyze well performance data from Eagle Ford shale gas wells provided by an operator. This analysis adopts the use of probabilistic rate transient analysis to help quantify uncertainty. With this approach, it is possible to systematically investigate the allowable parameter space based on acceptable ranges of inputs such as fracture length, matrix permeability, conductivity and well spacing. Since well spacing and reservoir boundaries were unknown, a base case with a reservoir width of 1500 feet was assumed. This analysis presents a workflow that integrates probabilistic and analytical modeling for shale gas wells in an unconventional reservoir. To validate the results between probabilistic and analytical modeling, a percent difference of less than 15% was assumed as an acceptable range for the ultimate recoverable forecasts. Understanding the effect of existing completion on the cumulative production is of great value to operators. This information helps them plan and optimize future completion designs while reducing operational costs. This study addresses the secondary objective by generating an Artificial Neural Network model. Using database from existing wells, a neural network model was successfully generated and completion effectiveness and optimization analysis was conducted. A good agreement between the predicted model output values and actual values (R2 = 0.99) validated the applicability of this model. A completion optimization study showed that wells drilled in condensate-rich zones required higher proppant and liquid volumes, whereas wells in gas-rich zones required closer cluster spacing. Analysis results helped to identify wells which were either under-stimulated or over-stimulated and appropriate recommendations were made.

With the emergence of liquid rich shale (LRS) plays like Eagle Ford and Northern Barnett, the petroleum industry needs a simple, easily applied technique that provides reliable estimates of future production rates in this kind of reservoir. There is no guarantee that methodology that has proved to work in gas reservoirs will necessarily be appropriate in LRS reservoirs. In this work, we found that without corrections of early data, the Stretched Exponential Production Decline (SEPD) model, designed for transient flow, usually produces pessimistic forecasts of future production. The Duong method, another transient model, may be reasonable during long term transient linear flow, but notably optimistic after boundary-dominated flow (BDF) appears. For wells in BDF, the Arps model provides reasonable forecasts, but the Arps model may not be accurate when applied to transient data. A hybrid of early transient and later BDF models proves to be a reasonable solution to the forecasting problem in LRS. In addition, use of diagnostic plots (like log-log rate-time and log-log rate-material balance time plots) improves confidence in flow regime identification and production forecasting. In some LRS's, BDF is observed within 12 months. In any case, it is essential to identify or to estimate the time to reach BDF and to discontinue use of transient flow models after BDF appears or is expected. We validated our methodology using "hindcast analysis"; that is, matching the first half of production history to determine model parameters, then forecasting the second half of history and comparing to observed production data. We also found that application of pressure-corrected rates in decline curve analysis (DCA) may substantially improve the interpretation of data from unconventional oil wells flowing under unstable operating conditions. Fetkovich (hydraulically fractured well) type curve analysis can be added to improve confidence in flow regime identification from diagnostic plots and to estimate the Arps hyperbolic exponent b from the matching b stem on the type curve, which can then be extrapolated to determine estimated ultimate recovery. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/152507

"This book is fast becoming the standard text in its field", wrote a reviewer in the Journal of Canadian Petroleum Technology soon after the first appearance of Dake's book. This prediction quickly came true: it has become the standard text and has been reprinted many times. The author's aim - to provide students and teachers with a coherent account of the basic physics of reservoir engineering - has been most successfully achieved. No prior knowledge of reservoir engineering is necessary. The material is dealt with in a concise, unified and applied manner, and only the simplest and most straightforward mathematical techniques are used. This low-priced paperback edition will continue to be an invaluable teaching aid for years to come.

As the shale revolution continues in North America, unconventional resource markets are emerging on every continent. In the next eight to ten years, more than 100,000 wells and one- to two-million hydraulic fracturing stages could be executed, resulting in close to one trillion dollars in industry spending. This growth has prompted professionals ex

Transient and fracture dominated flow regimes in tight permeability shale reservoirs with hydraulically fractured horizontal wells impose many unconventional challenges. These include execution of appropriate shale decline curve analysis and the optimization of hydrocarbons recovery. Additionally, short production profiles available are inadequate for accurate production decline analysis. This research assessed the effectiveness of Arps' decline curve analysis and recently established methods--power law exponential analysis, logistic growth analysis, Duong's method and the author's approach--to predict future production of horizontal wells in the Eagle Ford Shale. Simulation models investigated history matching, enhanced shale oil recovery, and drainage area beyond stimulated reservoir volume. Traditional Arps' hyperbolic method sufficiently analyzed past production rates, but inaccurately forecasted cumulative productions. The recent decline models show slight variations in their past performance evaluations and forecasting future production trends. The technique proposed and used in this work enhanced the successful application of Arps' hyperbolic decline from 32.5% to 80%. Simulation results indicate 4.0% primary oil recovery factor and 5.8% enhanced shale oil recovery factor using CO2 miscible injection. Based on pressure observed outside of the stimulated reservoir volume, limited to the range of data used in this study, drainage area outside stimulated reservoir volume is not significant.

Superposition time functions offer one of the effective ways of handling variable-rate data. However, they can also be biased and misleading the engineer to the wrong diagnosis and eventually to the wrong analysis. Since the superposition time functions involve rate as essential constituent, the superposition time is affected greatly with rate issues. Production data of shale gas wells are usually subjected to operating issues that yield noise and outliers. Whenever the rate data is noisy or contains outliers, it will be hard to distinguish their effects from common regime if the superposition time functions are used as plotting time function on log-log plots. Such deceiving presence of these flow regimes will define erroneous well and reservoir parameters. Based on these results and with the upsurge of energy needs there might be some costly decisions will be taken such as refracting or re-stimulating the well especially in tight formations. In this work, a simple technique is presented in order to rapidly check whether there is data bias on the superposition-time specialized plots or not. The technique is based on evaluating the kernel of the superposition time function of each flow regime for the maximum production time. Whatever beyond the Kernel-Equivalent Maximum Production Time (KEMPT) it is considered as biased data. The hypothesis of this technique is that there is no way to see in the reservoir more than what has been seen. A workflow involving different diagnostic and filtering techniques has been proposed to verify proposed notion. Different synthetic and field examples were used in this study. Once the all problematic issues have been detected and filtered out, it was clear that whatever went beyond the KEMPT is a consequence of these issues. Thus, the proposed KEMPT technique can be relied on in order to detect and filter out the biased data points on superposition-time log-log plots. Both raw and filtered data were analyzed using type-curve matching of linear flow type-curves for calculating the original gas in-place (OGIP). It has been found that biased data yield noticeable reduced OGIP. Such reduction is attributed to the early fictitious onset of boundary dominated flow, where early false detection of the drainage boundaries defines less gas in-place occupied in these boundaries.

A comprehensive overview of the key geologic, geomechanical and engineering principles that govern the development of unconventional oil and gas reservoirs. Covering hydrocarbon-bearing formations, horizontal drilling, reservoir seismology and environmental impacts, this is an invaluable resource for geologists, geophysicists and reservoir engineers.

Unconventional gas reservoirs, such as tight gas and shale gas, appear to have great potential to supply future demand for hydrocarbon. Economics of these reservoirs are tied closely to the performance of multi-fractured horizontal wells (MFHWs), which is the most direct indicator of stimulation effectiveness. Thus, greater understanding and analysis of the factors affecting performance of MFHWs are critical for the efficient exploitation of such reservoirs. Hydrocarbon production data analysis (PDA) techniques have been commonly used to characterize hydraulic fracture (HF) and, ultimately, to evaluate hydraulic-fracturing jobs. Recent studies have shown that rate transient analysis of flowback data can also provide early insight into HF attributes. While PDA methods seek long-time production data, flowback analysis can be conducted using early water and gas production data obtained immediately after the completion of stimulation jobs. However, in comparison with the long-term hydrocarbon production period, the physics of the process is more difficult to capture during flowback production because of its short duration, at which one or more flow regimes may occur. In addition, the flowback flow system could be single- or two-phase, depending on reservoir type. According to reported field data, single-phase flowback can be observed in tight sands, but two-phase flow is expected in the case of shale gas. Although various mathematical models have been proposed to analyze single-phase (water) and two-phase (gas and water) flowback data, analytical models for interpretation of data are still at an early stage of development. The objectives of this study are first to reproduce the relevant analytical models available in literature and understand their advantages and limitations; then, to develop single-phase and two-phase analytical models capable of predicting HF attributes such as fracture half-length and fracture permeability using early water and gas production data. In this study, a set of numerical simulations was conducted using CMG (IMEX) to examine the capacity of available mathematical models. It was found that most of the single-phase flowback models in the literature are accurate only under pseudo steady-state conditions, where a boundary-dominated flow regime with a constant production rate has been established. Another limitation of current models is that they can only estimate one fracture attributes: kf or xf. Knowing the shortcomings of current models, I developed a set of analytical models for both single- and two-phase systems, which were validated against numerical simulations. The single-phase model can closely estimate HF attributes, such as permeability and half-length under constant pressure as well as constant flowrate condition, for both transient and boundary dominated flow periods. Furthermore, I extended the developed single-phase model to variable bottomhole conditions by employing superposition principle. In the case of two-phase flow system, I developed an analytical model under fracture depletion mechanism for both early gas production (EGP) and late gas production (LGP) periods. In the case of EGP, gas flux from matrix to HF is assumed to be negligible. Comparisons of numerical results with those obtained from the analytical model show that the developed two-phase model for EGP can accurately predict fracture attributes. In the case of LGP, a coupled model is developed to include the effect of gas influx from matrix to HF on flowback data, where a uniform pressure decline rate is assumed in fracture-matrix system. The two-phase model has the advantage of linear behavior of water properties and avoids the computational complexity. With typical Barnett shale properties input in the numerical simulation, the analytical model can accurately estimate fracture attributes within a 10% error margin. Sensitivity analyses of fracture conductivity and initial water saturation in fracture have been conducted to illustrate the validity of two-phase flowback model applied in LGP. The results reveal that, within the physical range of fracture conductivity and initial water saturation, the two-phase flowback model can accurately evaluate fracture attributes. However, the model is more accurate for cases with smaller fracture conductivity and higher initial water saturation in fracture.

Shale Gas and Tight Oil Reservoir Simulation delivers the latest research and applications used to better manage and interpret simulating production from shale gas and tight oil reservoirs. Starting with basic fundamentals, the book then includes real field data that will not only generate reliable reserve estimation, but also predict the effective range of reservoir and fracture properties through multiple history matching solutions. Also included are new insights into the numerical modelling of CO2 injection for enhanced oil recovery in tight oil reservoirs. This information is critical for a better understanding of the impacts of key reservoir properties and complex fractures. - Models the well performance of shale gas and tight oil reservoirs with complex fracture geometries - Teaches how to perform sensitivity studies, history matching, production forecasts, and economic optimization for shale-gas and tight-oil reservoirs - Helps readers investigate data mining techniques, including the introduction of nonparametric smoothing models