Petroleum exploration and development are capital intensive and smart economic decisions that need to be made to profitably extract oil and gas from the reservoirs. Accurate quantification of uncertainty in production forecasts will help in assessing risk and making good economic decisions. This study investigates the effect of combining dynamic data with the uncertainty in static data to see the effect on estimates of uncertainty in production forecasting. Fifty permeability realizations were generated for a reservoir in west Texas from available petrophysical data. We quantified the uncertainty in the production forecasts using a likelihood weighting method and an automatic history matching technique combined with linear uncertainty analysis. The results were compared with the uncertainty predicted using only static data. We also investigated approaches for best selecting a smaller number of models from a larger set of realizations to be history matched for quantification of uncertainty. We found that incorporating dynamic data in a reservoir model will result in lower estimates of uncertainty than considering only static data. However, incorporation of dynamic data does not guarantee that the forecasted ranges will encompass the true value. Reliability of the forecasted ranges depends on the method employed. When sampling multiple realizations of static data for history matching to quantify uncertainty, a sampling over the entire range of realization likelihoods shows larger confidence intervals and is more likely to encompass the true value for predicted fluid recoveries, as compared to selecting the best models.

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Recently, there has been considerable interest in the study of coupled fluid flow – geomechanics simulation, integrated into reservoir engineering. One of the most challenging problems in the petroleum industry is the understanding and predicting of subsidence at the surface due to formation compaction at depth, the result of withdrawal of fluid from a reservoir. In some oil fields, the compacting reservoir can support oil and gas production. However, the effects of compaction and subsidence may be linked to expenditures of millions of dollars in remedial work. The phenomena can also cause excessive stress at the well casing and within the completion zone where collapse of structural integrity could lead to loss of production. In addition, surface subsidence can result in problems at the wellhead or with pipeline systems and platform foundations. Recorded practice reveals that although these problems can be observed and measured, the technical methods to do this involve time, expense, with consideration uncertainty in expected compaction and are often not carried out. Alternatively, prediction of compaction and subsidence can be done using numerical reservoir simulation to estimate the extent of damage and assess measurement procedures. With regard to reservoir simulation approaches, most of the previous research and investigations are based on deterministic coupled theory applied to continuum porous media. In this work, uncertainty of parameters in reservoir is also considered. This thesis firstly investigates and reviews fully coupled fluid flow – geomechanics modeling theory as applied to reservoir engineering and geomechanics research. A finite element method is applied for solving the governing fully coupled equations. Also simplified analytical solutions that present more efficient methods for estimating compaction and subsidence are reviewed. These equations are used in uncertainty and stochastic simulations. Secondly, porosity and permeability variations can occur as a result of compaction. The research will explore changes of porosity and permeability in stress sensitive reservoirs. Thirdly, the content of this thesis incorporates the effects of large structures on stress variability and the impact of large structural features on compaction. Finally, this thesis deals with affect of pore collapse on multiphase fluid and rock properties. A test case from Venezuelan field is considered in detail; investigating reservoir performance and resultant compaction and subsidence. The research concludes that the application of coupled fluid flow – geomechanics modeling is paramount in estimating compaction and subsidence in oil fields. The governing equations that represent behaviour of fluid flow and deformation of the rock have been taken into account as well as the link between increasing effective stress and permeability/porosity. From both theory and experiment, this thesis shows that the influence of effective stress on the change in permeability is larger than the effect of reduction in porosity. In addition, the stochastic approach used has the advantage of covering the impact of uncertainty when predicting subsidence and compaction. This thesis also demonstrates the influence of a large structure (i.e. a fault) on stress regimes. Mathematical models are derived for each fault model to estimate the perturbed stress. All models are based on Mohr–Coulomb’s failure criteria in a faulted area. The analysis of different stress regimes due to nearby faults shows that effective stress regimes vary significantly compared to a conventional model. Subsequently, the selection of fault models, fault friction, internal friction angle and Poisson’s ratio are most important to assess the influence of the discontinuity on the reservoir compaction and subsidence because it can cause a significant change in stress regimes. To deal with multiphase flow in compacting reservoirs, this thesis presents a new method to generate the relative permeability curves in a compacting reservoir. The principle for calculating the new values of irreducible water saturation (Swir) due to compaction is demonstrated in this research. Using coupled reservoir simulators, fluid production due to compaction is simulated more comprehensively. In the case example presented, water production is reduced by approximately 70% compared to conventional modeling which does not consider changes in relative permeability. This project can be extended by applying the theory and practical methodologies developed to other case studies, where compaction and stress sensitivity dominate the drive mechanism. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1374653 / Thesis (Ph.D.) - University of Adelaide, Australian School of Petroleum, 2007

In this work, two topics of reservoir simulations are discussed. The first topic is the two-phase compositional flow simulation in hydrocarbon reservoir. The major obstacle that impedes the applicability of the simulation code is the long run time of the simulation procedure, and thus speeding up the simulation code is necessary. Two means are demonstrated to address the problem: parallelism in physical space and the application of sparse grids in parameter space. The parallel code can gain satisfactory scalability, and the sparse grids can remove the bottleneck of flash calculations. Instead of carrying out the flash calculation in each time step of the simulation, a sparse grid approximation of all possible results of the flash calculation is generated before the simulation. Then the constructed surrogate model is evaluated to approximate the flash calculation results during the simulation. The second topic is the wormhole propagation simulation in carbonate reservoir. In this work, different from the traditional simulation technique relying on the Darcy framework, we propose a new framework called Darcy-Brinkman-Forchheimer framework to simulate wormhole propagation. Furthermore, to process the large quantity of cells in the simulation grid and shorten the long simulation time of the traditional serial code, standard domain-based parallelism is employed, using the Hypre multigrid library. In addition to that, a new technique called “experimenting field approach” to set coefficients in the model equations is introduced. In the 2D dissolution experiments, different configurations of wormholes and a series of properties simulated by both frameworks are compared. We conclude that the numerical results of the DBF framework are more like wormholes and more stable than the Darcy framework, which is a demonstration of the advantages of the DBF framework. The scalability of the parallel code is also evaluated, and good scalability can be achieved. Finally, a mixed finite element scheme is proposed for the wormhole simulation.

Numerical reservoir simulation has been used to describe mechanism of methane gas desorption process, diffusion process, and fluid flow in a coalbed methane reservoir. The reservoir simulation model reflects the response of a reservoir system and the relationship among coalbed methane reservoir properties, operation procedures, and gas production. This work presents a procedure to select the optimum well spacing scenario by using a reservoir simulation. This work uses a two-phase compositional simulator with a dual porosity model to investigate well-spacing effects on coalbed methane production performance and methane recovery. Because of reservoir parameters uncertainty, a sensitivity and parametric study are required to investigate the effects of parameter variability on coalbed methane reservoir production performance and methane recovery. This thesis includes a reservoir parameter screening procedures based on a sensitivity and parametric study. Considering the tremendous amounts of simulation runs required, this work uses a regression analysis to replace the numerical simulation model for each wellspacing scenario. A Monte Carlo simulation has been applied to present the probability function. Incorporated with the Monte Carlo simulation approach, this thesis proposes a well-spacing study procedure to determine the optimum coalbed methane development scenario. The study workflow is applied in a North America basin resulting in distinct Net Present Value predictions between each well-spacing design and an optimum range of well-spacing for a particular basin area.

The main objective of this research is to devise a risk-based methodology for developing emergency operation schedules (EOS). EOS are decision tools that provide guidance to reservoir operators in charge of making real-time release decisions during major flood events. A computer program named REOS was created to perform the computations to develop risk-based EOS. The computational algorithm in REOS is divided in three major components: (1) synthetic streamflow generation, (2) mass balance computations, and (3) frequency analysis. The methodology computes the required releases to limit storage to the capacity available based on the probabilistic properties of future flows, conditional to current streamflow conditions. The final product is a series of alternative risk-based EOS in which releases, specified as a function of reservoir storage level, current and past inflows, and time of year, are associated with a certain risk of failing to attain the emergency operations objectives. The assumption is that once emergency operations are triggered by a flood event, the risk associated with a particular EOS reflects the probability of exceeding a pre-established critical storage level given that the same EOS is followed throughout the event. This provides reservoir operators with a mechanism for evaluating the tradeoffs and potential consequences of release decisions. The methodology was applied and tested using the Addicks and Barker Reservoir system in Houston, TX as a case study. Upstream flooding is also a major concern for these reservoirs. Modifications to the current emergency policies that would allow emergency releases based on the probability of upstream flooding are evaluated. Riskbased EOS were tested through a series of flood control simulations. The simulations were performed using the HEC-ResSim reservoir simulation model. Rainfall data recorded from Tropical Storm Allison was transposed over the Addicks and Barker watersheds to compute hypothetical hydrographs using HEC-HMS. Repeated runs of the HEC-ResSim model were made using different flooding and residual storage scenarios to compare regulation of the floods under alternative operating policies. An alternative application of the risk-based EOS in which their associated risk was used to help quantify the actual probability of upstream flooding in Addicks and Barker was also presented.

Developing hybrid processes for heavy oil recovery is a major area of interest in recent years. The need for such processes originates from the challenges of heavy oil recovery relating to fluid injectivity, reservoir heating, and oil displacement and production. These challenges are particularly profound in shaley thin oil deposits where steam injection is not feasible and other recovery methods should be employed. In this work, we aim to develop and optimize a hybrid process that involves moderate reservoir heating and chemical enhanced oil recovery (EOR). This process, in its basic form, is a three-stage scheme. The first stage is a short electrical heating, in which the reservoir temperature is raised just enough to create fluid injectivity. After electrical heating has created sufficient fluid injectivity, high-rate high-pressure hot water injection accelerates the raise in temperature of the reservoir and assists oil production. At the end of hot waterflooding the oil viscosities are low enough for an Alkali-Co-solvent-Polymer (ACP) chemical flood to be performed where oil can efficiently be mobilized and displaced at low pressure gradients. A key aspect of ultra-low IFT chemical flood, such as ACP, is the rheology of the microemulsions that form in the reservoir. Undesirable rheology impedes the displacement of the chemical slug in the reservoir and results in poor process performance or even failure. The viscosity of microemulsions can be altered by the addition of co-solvents and branched or twin-tailed co-surfactants and by an increase in temperature. To reveal the underlying mechanisms, a consistent theoretical framework was developed. Employing the membrane theory and electrostatics, the significance of charge and/or composition heterogeneity in the interface membrane and the relevance of each to the above-mentioned alteration methods was demonstrated. It was observed that branched co-surfactants (in mixed surfactant formulations) and temperature only modify the saddle-splay modulus (k ̅) and bending modulus (k) respectively, whereas co-solvent changes both moduli. The observed rheological behavior agrees with our findings. To describe the behavior of microemulsions in flow simulations, a rheological model was developed. A key feature of this model is the treatment of the microemulsion as a bi-network. This provides accuracy and consistency in the calculation of the zero-shear viscosity of a microemulsion regardless of its type and microstructure. Once model parameters are set, the model can be used at any concentration and shear rate. A link between the microemulsion rheological behavior and its microstructure was demonstrated. The bending modulus determines the magnitude of the viscous dissipations and the steady-shear behavior. The new model, additionally, includes components describing the effects of rheology alteration methods. Experimental viscosity data were used to validate the new microemulsion viscosity model. Several ACP corefloods showing the large impact of microemulsion viscosity on process performance were matched using the UTCHEM simulator with the new microemulsion rheology model added to the code. Finally, numerical simulations based on Peace River field data were performed to investigate the performance of the proposed hybrid thermal-chemical process. Key design parameters were identified to be the method of heating, duration of the heating, ACP slug size and composition, polymer drive size, and polymer concentration in the polymer drive. An optimization study was done to demonstrate the economic feasibility of the process. The optimization revealed that short electrical heating and high-rate high-pressure waterflooding are necessary to minimize the energy use and operational expenses. The optimum slug and polymer drive sizes were found to be ~0.25 PV and ~1 PV, respectively. It was shown that the well costs dominate the expenditure and the overall cost of the optimized process is in the range of 20-30 $⁄bbl of incremental oil production. / text

Slickwater with sand is the most commonly used hydraulic fracturing treatment for shale reservoirs. The slickwater treatment produces long skinny fractures, but only the near wellbore region is propped due to fast settling of sand. Adding gel into water can prevent the fast settling of sand, but gel may damage the fracture surface and proppant pack. Moreover, current water-based fracturing consumes a large amount of water, has high water leakage, and imposes high water disposal costs. The goal of this project is to develop non-damaging, less water-intensive fracturing treatments for shale gas reservoirs with improved proppant placement efficiency. Earlier studies have proposed to replace sand with ultra-light weight proppants (ULWP) to enhance proppant transport, but it is not used commonly in field. This study evaluates the performance of three kinds of ULWPs covering a wide range of specific gravity and representing the three typical manufacturing methods. In addition to replacing sand with ULWPs, replacing water with foams can be an alternative treatment that reduces water usage and decreases proppant settling. Polymer-added foams have been used in conventional reservoirs to improve proppant placement efficiency. However, polymers can damage shale permeability in unconventional reservoirs. This dissertation studies polymer-free foams (PFF) and evaluates their performance. This study uses both experiments and simulations to assess the productivity and profitability of the ULWP treatment and PFF treatment. First, a reservoir simulation model is built in CMG to study the impact of fracture conductivity and propped length on fracture productivity. This model assumes a single fracture intersecting a few reactivated natural fractures. Second, a 2D fracturing model is used to simulate the fracture propagation and proppant transport. Third, strength, API conductivity and gravity settling rates are measured for three ULWPs. Fourth, foam stability tests are conducted to screen the best PFF agents and the selected foams are put into a circulating loop to study their rheology. Finally, empirical correlations from the experiments are applied in the fracturing model and reservoir model to predict productivity by using the ULWPs with slickwater or using the PFFs with sand. Experimental results suggest that, at 4000 psi with concentrations varying from partial monolayer (0.05 lb/ft²) to multilayer (1 lb/ft²), ULW-1 (polymeric) is the most deformable with conductivity of 1-10 md-ft. ULW-2 (resin coated and impregnated ground walnut hull) is the second most deformable with similar conductivity. ULW-3 (resin coated porous ceramic) is the least deformable with conductivity of 20-1000 md-ft, which is comparable to sand. Three foam formulations (A, B: regular surfactant foam, C: viscoelastic surfactant foam) are selected based on the stability results of fourteen surfactants. All PFFs exhibit power-law rheological behavior in a laminar flow regime. The power law parameters of the regular surfactant PFF depend on both quality and pressure when quality is higher than 60% but depend on quality only when quality is lower than 60%. Simulation results suggest that under the optimal concentration of 0.04-0.06 v/v (0.37-0.55 lb/gal) for both ULW-1 and ULW-2, and 0.1 v/v (1.46 lb/gal) for ULW-3, 1-year cumulative production for 0.1 µD shale reservoir is higher than sand by 127% for ULW-1, 28% for ULW-2, and 38% for ULW-3. The productivity benefits decrease as shale permeability increases for all three ULWPs. ULW-1 and ULW-2 have higher productivity benefits for longer production time, while ULW-3 has relatively constant productivity benefits over time. The economic profit of ULW-1 when priced at $5/lb is 2.2 times larger than that of sand for 1-year production in 0.1 µD shale reservoirs; the acceptable maximum price is $10/lb for ULW-1, $6/lb for ULW-2, and $2.5/lb for ULW-3. The maximum price increases as production time increases. The PFFs with a quality of 60% carrying mesh 40 sand at a partial monolayer concentration of 0.04 v/v (0.88 lb/gal) can generate 50% higher productivity, 74% higher economic profit, and over 300% higher water efficiency than the best slickwater-sand case (mesh 40 sand at 0.1 v/v) for 1-year production in 0.1µD shale reservoirs. The benefits of using the PFFs decrease with increasing shale permeability, increasing production time, or decreasing pumping time. This dissertation gives a range of field conditions where the ULWP and PFF may be more effective than slickwater-sand fracturing. / text

The primary controls on subsurface reservoir heterogeneities and fluid flow characteristics are sedimentary facies architecture and petrophysical rock fabric distribution in clastic reservoirs and in carbonate reservoirs, respectively. Facies models are critical and fundamental for summarizing facies and facies architecture in data-rich areas. Facies models also assist in predicting the spatial architectural trend of sedimentary facies in other areas where subsurface information is lacking. The method for transferring geological information from different facies models into digital data and then generating associated numerical models is called facies modeling or geological modeling. Facies modeling is also vital to reservoir simulation and reservoir characterization analysis. By extensively studying and reviewing the relevant research in the published literature, this report identifies and analyzes the best and most detailed geologic data that can be used in facies modeling, and the most current geostatistical and stochastic methods applicable to facies modeling. Through intensive study of recent literature, the author (1) summarizes the basic concepts and their applications to facies and facies models, and discusses a variety of numerical modeling methods, including geostatistics and stochastic facies modeling, such as variogram-based geostatistics modeling, object-based stochastic modeling, and multiple-point geostatistics modeling; and (2) recognizes that the most effective way to characterize reservoir is to integrate data from multiple sources, such as well data, outcrop data, modern analogs, and seismic interpretation. Detailed and more accurate parameters using in facies modeling, including grain size, grain type, grain sorting, sedimentary structures, and diagenesis, are gained through this multidisciplinary analysis. The report concludes that facies and facies models are scale dependent, and that attention should be paid to scale-related issues in order to choose appropriate methods and parameters to meet facies modeling requirements. / text

Quantifying the drilling or recompletion potential in producing gas basins is often a challenging problem, because of large variability in rock quality, well spacing, and well completion practices, and the large number of wells involved. Complete integrated reservoir studies to determine infill potential are often too time-consuming and costly for many producing gas basins. In this work we evaluate the accuracy of a statistical moving-window technique that has been used in tight-gas formations to assess infill and recompletion potential. The primary advantages of the technique are its speed and its reliance upon well location and production data only. We used the statistical method to analyze simulated low-permeability, 100-well production data sets, then compared the moving-window infill-well predictions to those from reservoir simulation. Results indicate that moving-window infill predictions for individual wells can be off by more than 50%; however, the technique accurately predicts the combined infill-production estimate from a group of infill candidates, often to within 10%. We found that the accuracy of predicted infill performance decreases as heterogeneity increases and increases as the number of wells in the project increases. The cases evaluated in this study included real-world well spacing and production rates and a significant amount of depletion at the infill locations. Because of its speed, accuracy and reliance upon readily available data, the moving window technique can be a useful screening tool for large infill development projects.