Various analytical, semi-analytical, and empirical models have been proposed to characterize rate and pressure behavior as a function of time in tight/shale gas systems featuring a horizontal well with multiple hydraulic fractures. Despite a small number of analytical models and published numerical studies there is currently little consensus regarding the large-scale flow behavior over time in such systems. The purpose of this work is to construct a fit-for-purpose numerical simulator which will account for a variety of production features pertinent to these systems, and to use this model to study the effects of various parameters on flow behavior. Specific features examined in this work include hydraulically fractured horizontal wells, multiple porosity and permeability fields, desorption, and micro-scale flow effects. The theoretical basis of the model is described in Chapter I, along with a validation of the model. We employ the numerical simulator to examine various tight gas and shale gas systems and to illustrate and define the various flow regimes which progressively occur over time. We visualize the flow regimes using both specialized plots of rate and pressure functions, as well as high-resolution maps of pressure distributions. The results of this study are described in Chapter II. We use pressure maps to illustrate the initial linear flow into the hydraulic fractures in a tight gas system, transitioning to compound formation linear flow, and then into elliptical flow. We show that flow behavior is dominated by the fracture configuration due to the extremely low permeability of shale. We also explore the possible effect of microscale flow effects on gas effective permeability and subsequent gas species fractionation. We examine the interaction of sorptive diffusion and Knudsen diffusion. We show that microscale porous media can result in a compositional shift in produced gas concentration without the presence of adsorbed gas. The development and implementation of the micro-flow model is documented in Chapter III. This work expands our understanding of flow behavior in tight gas and shale gas systems, where such an understanding may ultimately be used to estimate reservoir properties and reserves in these types of reservoirs.

The ever increasing energy demand brings about widespread interest to rapidly, profitably and efficiently develop unconventional resources, among which tight gas sands hold a significant portion. However, optimization of development strategies in tight gas fields is challenging, not only because of the wide range of depositional environments and large variability in reservoir properties, but also because the evaluation often has to deal with a multitude of wells, limited reservoir information, and time and budget constraints. Unfortunately, classical full-scale reservoir evaluation cannot be routinely employed by small- to medium-sized operators, given its timeconsuming and expensive nature. In addition, the full-scale evaluation is generally built on deterministic principles and produces a single realization of the reservoir, despite the significant uncertainty faced by operators. This work addresses the need for rapid and cost-efficient technologies to help operators determine optimal well spacing in highly uncertain and risky unconventional gas reservoirs. To achieve the research objectives, an integrated reservoir and decision modeling tool that fully incorporates uncertainty was developed. Monte Carlo simulation was used with a fast, approximate reservoir simulation model to match and predict production performance in unconventional gas reservoirs. Simulation results were then fit with decline curves to enable direct integration of the reservoir model into a Bayesian decision model. These integrated tools were applied to the tight gas assets of Unconventional Gas Resources Inc. in the Berland River area, Alberta, Canada.

One of the key challenges in the Oil & Gas industry is to best manage reservoirs under different conditions, constrained by production rates based on various economic scenarios, in order to meet energy demands and maximize profit. To address the energy demand challenges, a transformation in the paradigm of the utilization of "real-time" data has to be brought to bear, as one changes from a static decision making to a dynamical and data-driven management of production in conjunction with real-time risk assessment. The use of modern methods of computational modeling and simulation may be the only means to account for the two major tasks involved in this paradigm shift: (1) large-scale computations; and (2) efficient utilization of the deluge of data streams. Recently, history matching and optimization were brought together in the oil industry into an integrated and more structured approach called optimal closed-loop reservoir management. Closed-loop control algorithms have already been applied extensively in other engineering fields, including aerospace, mechanical, electrical and chemical engineering. However, their applications to porous media flow, such as - in the current practices and improvements in oil and gas recovery, in aquifer management, in bio-landfill optimization, and in CO2 sequestration have been minimal due to the large-scale nature of existing problems that generate complex models for controller design and real-time implementation. Their applicability to a realistic field is also an open topic because of the large-scale nature of existing problems that generate complex models for controller design and real-time implementation, hindering its applicability. Basically, three sources of high-dimensionality can be identified from the underlying reservoir models: size of parameter space, size of state space, and the number of scenarios or realizations necessary to account for uncertainty. In this paper we will address type problem of high dimensionality by focusing on the mitigation of the size of the state-space models by means of model-order reduction techniques in a systems framework. We will show how one can obtain accurate reduced order models which are amenable to fast implementations in the closed-loop framework .The research will focus on System Identification (System-ID) (Jansen, 2009) and Model Predictive Control (MPC) (Gildin, 2008) to serve this purpose. A mathematical treatment of System-ID and MPC as applied to reservoir simulation will be presented. Linear MPC would be studied on two specific reservoir models after generating low-order reservoir models using System-ID methods. All the comparisons are provided from a set of realistic simulations using the commercial reservoir simulator called Eclipse. With the improvements in oil recovery and reductions in water production effectively for both the cases that were considered, we could reinforce our stance in proposing the implementation of MPC and System-ID towards the ultimate goal of "real-time" production optimization.

In this study, a three-dimensional, three-phase dynamic simulation model based on geological investigations of Korolev oilfield in Kazakhstan was used as a development planning tool in order to improve performance of three new wells. The model, developed previously by means of a seismic study, well log and core data, incorporating with characteristics of oilfield productivity, properties of reservoir, liquids and gases that are saturating the hydrocarbon-bearing horizon can be used to calculate development parameters for Korolev field, including production well locations, drilling schedules, and to facilitate both long-term and short-term forecasting for the purposes of optimizing the hydrocarbon recovery from the field. The objective of this work is to assess the impact of adding 3 extra producing wells and find ways to optimize cumulative production with the least impact on the existing development plan by means of deeper understanding subsurface dynamic processes of the Korolev field. The challenge is a high degree of connectivity between wells in the productive formation throughout the field so that any change of production parameters affects the whole field&rsquo / s cumulative production. Trying to find a solution to optimum production of the reservoir forecast studies were carried out, the impact of each new well on development parameters was defined, sub-surface processes changes due to extra producers lead-in were explained and as a result of this thesis two optimization models were proposed, one of which will bring nearly 9.7 million barrels more oil.

Nowadays, field development strategy has become increasingly dependent on the results of reservoir simulation models. Reservoir studies demand fast and efficient results to make investment decisions that require a reasonable trade off between accuracy and simulation time. One of the suitable options to fulfill this requirement is streamline reservoir simulation technology, which has become very popular in the last few years. Streamline (SL) simulation provides an attractive alternative to conventional reservoir simulation because SL offers high computational efficiency and minimizes numerical diffusion and grid orientation effects. However, streamline methods have weaknesses incorporating complex physical processes and can also suffer numerical accuracy problems. The main objective of this research is to evaluate the numerical accuracy of the latest SL technology, and examine the influence of different factors that may impact the solution of SL simulation models. An extensive number of numerical experiments based on sensitivity analysis were performed to determine the effects of various influential elements on the stability and results of the solution. Those experiments were applied to various models to identify the impact of factors such as mobility ratios, mapping of saturation methods, number of streamlines, time step sizes, and gravity effects. This study provides a detailed investigation of some fundamental issues that are currently unresolved in streamline simulation.

Reconciling high-resolution geologic models to field production history is still a very time-consuming procedure. Recently streamline-based assisted and automatic history matching techniques, especially production data integration by “travel-time matching,” have shown great potential in this regard. But no systematic study was done to examine the merits of travel-time matching compared to more traditional amplitude matching for field-scale application. Besides, most applications were limited to two-phase water-oil flow because current streamline models are limited in their ability to incorporate highly compressible flow in a rigorous and computationally efficient manner. The purpose of this work is fourfold. First, we quantitatively investigated the nonlinearities in the inverse problems related to travel time, generalized travel time, and amplitude matching during production data integration and their impact on the solution and its convergence. Results show that the commonly used amplitude inversion can be orders of magnitude more nonlinear compared to the travel-time inversion. Both the travel-time and generalized travel time inversion (GTTI) are shown to be more robust and exhibit superior convergence characteristics. Second, the streamline-based assisted history matching was enhanced in two important aspects that significantly improve its efficiency and effectiveness. We utilize streamline-derived analytic sensitivities to determine the location and magnitude of the changes to improve the history match, and we use the iterative GTTI for model updating. Our approach leads to significant savings in time and manpower. Third, a novel approach to history matching finite-difference models that combines the efficiency of analytical sensitivity computation of the streamline models with the versatility of finite-difference simulation was developed. Use of finite-difference simulation can account for complex physics. Finally, we developed an approach to history matching three-phase flow using a novel compressible streamline formulation and streamline-derived analytic sensitivities. Streamline models were generalized to account for compressible flow by introducing a relative density of total fluids along streamlines and a density-dependent source term in the saturation equation. The analytical sensitivities are calculated based on the rigorous streamline formulation. The power and utility of our approaches have been demonstrated using both synthetic and field examples.

The demand for oil and gas is increasing yearly, whereas proven oil and gas reserves are being depleted. The potential of stripper oil and gas fields to supplement the national energy supply is large. In 2006, stripper wells accounted for 15% and 8% of US oil and gas production, respectively. With increasing energy demand and current high oil and gas prices, integrated reservoir studies, secondary and tertiary recovery methods, and infill drilling are becoming more common as operators strive to increase recovery from stripper oil and gas fields. The primary objective of this research was to support optimized production of oil and gas from stripper well fields by evaluating one stripper gas field and one stripper oil field. For the stripper gas field, I integrated geologic and engineering data to build a detailed reservoir characterization model of the Second White Specks (SSPK) reservoir in Garden Plains field, Alberta, Canada. The objectives of this model were to provide insights to controls on gas production and to validate a simulation-based method of infill drilling assessment. SSPK was subdivided into Units A ? D using well-log facies. Units A and B are the main producing units. Unit A has better reservoir quality and lateral continuity than Unit B. Gas production is related primarily to porosity-netthickness product and permeability and secondarily to structural position, minor structural features, and initial reservoir pressure. For the stripper oil field, I evaluated the Green River formation in the Wells Draw area of Monument Butte field, Utah, to determine interwell connectivity and to assess optimal recovery strategies. A 3D geostatistical model was built, and geological realizations were ranked using production history matching with streamline simulation. Interwell connectivity was demonstrated for only major sands and it increases as well spacing decreases. Overall connectivity is low for the 22 reservoir zones in the study area. A water-flood-only strategy provides more oil recovery than a primary-then-waterflood strategy over the life of the field. For new development areas, water flooding or converting producers to injectors should start within 6 months of initial production. Infill drilling may effectively produce unswept oil and double oil recovery. CO2 injection is much more efficient than N2 and CH4 injection. Water-alternating-CO2 injection is superior to continuous CO2 injection in oil recovery. The results of this study can be used to optimize production from Garden Plains and Monument Butte fields. Moreover, these results should be applicable to similar stripper gas and oil field fields. Together, the two studies demonstrate the utility of integrated reservoir studies (from geology to engineering) for improving oil and gas recovery.

Simulation of large-scale and complex reservoirs requires fine and detailed gridding, which involves a significant amount of memory and is computationally expensive. Nowadays, clusters of PCs and high-performance computing (HPC) centers are widely available. These systems allow parallel processing, which helps large-scale simulations run faster and more efficient. In this research project, we developed a parallel version of The University of Texas Compositional Simulator (UTCOMP). The parallel UTCOMP is capable of running on both shared and distributed memory parallel computers. This parallelization included all physical features of the original code, such as higher-order finite difference, physical dispersion, and asphaltene precipitation. The parallelization was verified for several case studies using multiple processors. The parallel simulator produces outputs required for visualizing simulation results using the S3graph visualization software. The efficiency of the parallel simulator was assessed in terms of speedup using various numbers of processors. Subsequently, we improved the coding and implementation in the simulator in order to minimize the communications between the processors to improve the parallel efficiency to carry out the simulations. To improve the efficiency of the linear solver in the simulator, we implemented three well-known high-performance parallel solver packages (SAMG, Hypre, and PETSc) in the parallel simulator. Then, the performances of the solver packages were improved in terms of the input parameters for solving large-scale reservoir simulation problems. The developed parallel simulator has expanded the capability of the original code for simulating large-scale reservoir simulation case studies. In other words, with sufficient number of processors, a field-scale simulation with a million grid cells can be performed in few hours. Several case studies are presented to show the performance of the parallel simulator. / text

Hydraulic fracturing is an important stimulation method for producing unconventional gas reserves. Natural fractures are present in many low-permeability gas environments and often provide important production pathways for natural gas. The production benefit from natural fractures can be immense, but it is difficult to quantify. The Mesaverde Group in the Piceance Basin in Colorado is a gas producing reservoir that has low matrix permeability but is also highly naturally fractured. Wells in the Piceance Basin are hydraulically fractured, so the production enhancements due to natural fracturing and hydraulic fracturing are difficult to decouple. In this thesis, dipole sonic logs were used to quantify geomechanical properties by combining stress equations with critically-stressed faulting theory. The properties derived from this log-based evaluation were used to numerically model hydraulic fracture treatments that had previously been pumped in the basin. The results from these hydraulic fracture models, in addition to the log-derived reservoir properties were used to develop reservoir models. Several methods for simulating the reservoir were compared and evaluated, including layer cake models, geostatistical models, and models simulating the fracture treatment using water injection. The results from the reservoir models were compared to actual production data to quantify the effect of both hydraulic fractures and natural fractures on production. This modeling also provided a framework upon which completion techniques were economically evaluated. / text

Many engineering and scientific applications of flow in porous media are characterized by transport phenomena at multiple spatial scales, including pollutant transport, groundwater remediation, and acid injection to enhance well production. Carbon sequestration in particular is a multiscale problem, because the trapping and leakage mechanisms of CO2 in the subsurface occur from the sub-pore level to the basin scale. Quantitative and predictive pore-scale modeling has long shown to be a valuable tool for studying fluid-rock interactions in porous media. However, due to the size limitation of the pore-scale models (10-4-10-2m), it is impossible to model an entire reservoir at the pore scale. A straightforward multiscale approach would be to upscale macroscopic parameters (e.g. permeability) directly from pore-scale models and then input them into a continuum-scale simulator. However, it has been found that the large-scale models do not predict in many cases. One possible reason for the inaccuracies is oversimplified boundary conditions used in this direct upscaling approach. The hypothesis of this work is that pore-level flow and upscaled macroscopic parameters depends on surrounding flow behavior manifested in the form of boundary conditions. The detailed heterogeneity captured by the pore-scale models may be partially lost if oversimplified boundary conditions are employed in a direct upscaling approach. As a result, extracted macroscopic properties may be inaccurate. Coupling the model to surrounding media (using finite element mortars to ensure continuity between subdomains) would result in more realistic boundary conditions, and can thus improve the accuracy of the upscaled parameters. To test the hypothesis, mortar coupling is employed to couple pore-scale models and also couple pore-scale models to continuum models. Flow field derived from mortar coupling and direct upscaling are compared, preferably against a true solution if one exists. It is found in this dissertation that pore-scale flow and upscaled parameters can be significantly affected by the surrounding media. Therefore, using arbitrary boundary conditions such as constant pressure and no-flow boundaries may yield misleading results. Mortar coupling captures the detailed variation on the interface and imposes realistic boundary conditions, thus estimating more accurate upscaled values and flow fields. An advanced upscaling tool, a Super Permeability Tensor (SPT) is developed that contains pore-scale heterogeneity in greater detail than a conventional permeability tensor. Furthermore, a multiscale simulator is developed taking advantage of mortar coupling to substitute continuum grids directly with pore-scale models where needed. The findings from this dissertation can significantly benefit the understanding of fluid flow in porous media, and, in particular, CO2 storage in geological formations which requires accurate modeling across multiple scales. The fine-scale models are sensitive to the boundary conditions, and the large scale modeling of CO2 transport is sensitive to the CO2 behavior affected by the pore-scale heterogeneity. Using direct upscaling might cause significant errors in both the fine-scale and the large-scale model. The multiscale simulator developed in this dissertation could integrate modeling of CO2 physics at all relevant scales, which span the sub-pore or pore level to the basin scale, into one single simulator with effective and accurate communication between the scales. The multiscale simulator provides realistic boundary conditions for the fine scales, accurate upscaled information to continuum-scale, and allows for the distribution of computational power where needed, thus maintaining high accuracy with relatively low computational cost. / text