As oil and gas supply decrease, it becomes more important to quantify the uncertainty associated with reservoir models and implementation of field development decisions. Various geostatistical methods have assisted in the development of field scale models of reservoir heterogeneity. Sequential simulation algorithms in geostatistic require an assessment of local uncertainty in an attribute value at a location followed by random sampling from the uncertainty distribution to retrieve the simulation value. Instead of random sampling of an outcome from the uncertainty distrubution, the retrieval of an optimal simulated value at each location by considering an economic loss function is demonstrated in this thesis. By applying a loss function that depicts the economic impact of an over or underestimation at a location and retrieving the optimal simulated value that minimizes the expected loss, a map of simulated values can be generated that accounts for the impact of permeability as it relates to economic loss. Both an asymmetric linear loss function and a parabolic loss function models are investigated. The end result of this procedure will be a reservoir realization that exhibits the correct spatial characteristics (i.e. variogram reproduction) while, at the same time, exhibiting the minimum expected loss in terms of the parameters used to construct the loss function. The process detailed in this thesis provides an effective alternative whereby realizations in the middle of the uncertainty distribution can be directly retrieved by application of suitable loss functions. An extension of this method is to alter the loss function (so as to emphasize either under or over estimation), other realizations at the extremes of the global uncertainty distribution can also be retrieved, thereby eliminating the necessity for the generation of a large suite of realizations to locate the global extremes of the uncertainty distribution. / text

History matching is a process wherein changes are made to an initial geologic model of a reservoir, so that the predicted reservoir performance matches with the known production history. Changes are made to the model parameters which include rock and fluid parameters (viscosity, compressibility, relative permeability, etc.) or properties within the geologic model. Assisted History Matching (AHM) provides an algorithmic framework to minimize the mismatch in simulation, and aids in accelerating this process. The changes made by AHM techniques, however, cannot ensure a geologically consistent reservoir model. In fact, the performance of these techniques depends on the initial starting model. In order to understand the impact of the initial model, this project explored the performance of the AHM approach using a specific field case, but working with multiple distinct geologic scenarios. This project involved an integrated seismic to simulation study, wherein I interpreted the seismic data, assembled the geological information, and performed petrophysical log evaluation along with well test data calibration. The ensemble of static models obtained was carried through the AHM methodology. I used sensitivity analysis to determine the most important dynamic parameters that affect the history match. These parameters govern the large scale changes in the reservoir description and are optimized using the Evolutionary Strategy Algorithm. Finally, the streamline based techniques were used for local modifications to match the water cut well by well. The following general conclusions were drawn from this study- a) The use of multiple simple geologic models is extremely useful in screening possible geologic scenarios and especially for discarding unreasonable alternative models. This was especially true for the large scale architecture of the reservoir. b) The AHM methodology was very effective in exploring a large number of parameters, running the simulation cases, and generating the calibrated reservoir models. The calibration step consistently worked better if the models had more spatial detail, instead of the simple models used for screening. c) The AHM methodology implemented a sequence of pressure and water cut history matching. An examination of specific models indicated that a better geologic description minimized the conflict between these two match criteria.

Reservoir characterization is very important in reservoir management to plan, monitor, predict and optimize oil production. Reservoir simulation is well-accepted in reservoir management but it requires many inputs, needs months to set up and complete a set of simulation runs, and contains large uncertainty in physical and geological properties. Therefore, simpler methods that provide quick results to complement or substitute reservoir simulation are important in decision making. Capacitance resistance model (CRM) is one of the methods. CRM is an input-output model derived from a continuity equation to quantify producer-injector connection strength during waterflood using solely production data. This work improves the CRM application method for waterflood and develops CRM theories and application methods for other recovery periods such as primary recovery and water-CO2 flood. A West Texas field test was carried out to validate CRM for a waterflood. The CRM fit was evaluated and used to optimize the oil production by changing injection rates. Through this first field experiment, a CRM application procedure was developed. With the CRM optimized injection schedule, the field gained 5372 bbls of additional oil production increase after one year. This research also quantitatively validates the CRM gain and time constant using synthetic fields and compares them to parameters of the streamline model, a complex model with similar purposes to the CRM. The CRM provides similar results as the streamline model with fewer inputs. The CRM was extended to primary recovery and water-CO2 flood. A new CRM equation – the integrated CRM (ICRM) - for primary recovery was developed and validated on many synthetic fields and an Oman field. The model can estimate dynamic pore volume, productivity index and average reservoir pressure that compare closely to simulated values and field knowledge. Additionally, the ability of CRM to quantify injector-producer connection strength and predict fluid production was examined on a synthetic water-CO2 flood field. A new oil production model to be used with CRM application in water-CO2 flood was developed and validated on synthetic data. The model predicts oil production from injection rate and relative permeability. CRM has successfully optimized waterflood on a West Texas field by reallocating the water from ineffective to effective injectors. New interpretations of the CRM parameters enable quantitative validation and integration of the CRM results with other methods. In primary recovery, the ICRM can estimate reservoir properties without requiring well testing which can cause loss of production. The CRM and the new oil production model can quickly characterize water-CO2 flood for short term production monitoring. / text

Advanced three-dimensional reservoir modeling was used to determine the optimum strategy for coalbed methane production in China's Qinshui Basin. Multiple completion strategies were analyzed for pre-mining methane drainage on the bases of economic, environmental, and mining-safety-based factors. Effective degasification in the Qinshui Basin is crucial to enhance the health and safety of the underground mining workforce and to decrease carbon dioxide equivalent greenhouse gas emissions. Active, large-scale degasification wells in the region include hydraulically stimulated vertical fracture wells and multilaterally drilled horizontal patterns, with the latter much less common. Reservoir modeling concludes that despite their limited implementation, horizontal coalbed methane drainage wells offer the benefits of faster reservoir depressurization, high gas production rates, and faster recovery times than traditional vertical fracture wells. Coupled with reservoir modeling results, discounted cash flow analyses show that high drilling density multilateral horizontal patterns are the most financially feasible degasification strategy in the Qinshui Basin, albeit a higher initial capital investment compared to traditional vertical fracture wells and lower drilling density horizontal patterns. Additionally, horizontal wellbore designs can be altered to account for varying permeability, enhancing the productivity of methane from reservoirs exhibiting permeability values less than 1 millidarcy. Furthermore, modeling suggests that proper orientation of select horizontal wellbore patterns is crucial to optimize recoverable reserves. Finally, a function was derived to represent the production rates of horizontal coalbed methane wells as a function of time. Analysis of the function's validity to actual production data and simulated production data suggest that it is most applicable in gassy coal seams up to 10 feet in thickness. The production rate curve was transformed to an analytical model, representing a function of well geometry and coal permeability as applied to other geological conditions of the Qinshui Basin. Scientific contributions associated with this research include: An in depth study of degasification associated with the Qinshui Basin's low permeability coals; The methodology for assessing environmental, safety and economic benefits of coal degasification; The relationship between lateral spacing and permeability to maintain substantial gas production rates; An improved production model to describe the entire producing period of coalbed methane wells. / Ph. D.

The focus of this research is to develop and extend the application of existing technologies to enhance seismic reservoir characterization. The chapters presented in this dissertation constitute five individual studies consisting of three applications of the Radon transform, one aspect of acoustic wave propagation, and a pilot study of generating a stochastic reservoir model. The first three studies focus on the use of the Radon transform to enhance surface-recorded, controlled-source seismic data. First, the use of this transform was extended to enhance diffraction patterns, which may be indicative of subsurface fractures. The geometry of primary reflections and diffractions on synthetic common-shot-gather data indicate that Radon filters can predict and model primary reflections upon inverse transformation. These modeled primaries can then be adaptively subtracted from the input gather to enhance the diffractions. Second, I examine the amplitude distortions at near and far offsets caused by free-surface multiple removal using Radon filters. These amplitudes are often needlessly reduced due to a truncation effect when the commonly used, unweighted least-squares solution is applied. Synthetic examples indicate that a weighted solution to the transformation minimizes this effect and preserves the reflection amplitudes. Third, a novel processing flow was developed to generate a stacked seismic section using the Radon transform. This procedure has the advantage over traditional summation of normal moveout corrected common midpoint gathers because it circumvents the need to perform manual and interpretive velocity analysis. The fourth study involves the detection of thin layers in periodic layerstacks. Numerical modeling of acoustic wave propagation suggests that the sinusoidal components of an incident signal with a wavelength that corresponds to the periodicity of the material be preferentially reflected. Isolating the different portions of the reflected wavefield and calculating the energy spectra may provide evidence of thin periodic layers which are deterministically unresolvable on their own. Object-based reservoir modeling often incorporates the use of lithology logs, deterministic seismic interpretation, architectural element analysis, geologic intuition, and modern and outcrop analogs. This last project consists of a pilot study where a more quantitative approach to define the statistical parameters currently derived through geologic intuition and analogs was developed. This approach utilizes a simulated annealing optimization technique for inversion and the pilot study shows that it can improve the correlation between synthesized and control logs. / Ph. D.

This work aims to enhance the Ensemble Kalman Filter performance by transforming the non-Gaussian state variables into Gaussian variables to be a step closer to optimality. This is done by using univariate and multivariate Box-Cox transformation. Some History matching methods such as Kalman filter, particle filter and the ensemble Kalman filter are reviewed and applied to a test case in the reservoir application. The key idea is to apply the transformation before the update step and then transform back after applying the Kalman correction. In general, the results of the multivariate method was promising, despite the fact it over-estimated some variables.

This thesis integrated geology, geophysics, and petroleum engineering data to build a detailed reservoir characterization models for three gas pay sands in the Grand Isle 33 & 43 fields, offshore Louisiana. The reservoirs are Late Miocene in age and include the upper (PM), middle (QH), and lower (RD) sands. The reservoir models address the stratigraphy of the upper (PM) sand and help delineate the lower (RD) reservoir. In addition, this research addresses the partially depleted QH-2 reservoir compartment. The detailed models were constructed by integrating seismic, well log, and production data. These detailed models can help locate recoverable oil and gas that has been left behind. The upper PM model further delineated that the PM sand has several areas that are shaled-out effectively creating a flow barrier within reservoir compartments. Due to the barrier in the PM-1 reservoir compartment, an area of potentially recoverable hydrocarbons remains. In Grand Isle 33, the middle QH sand was partially depleted in the QH-2 reservoir compartment by a series of development wells. Bottom hole pressure data from wells in Grand Isle 32 & 33 reveal that the two QH fault compartments are in communication across a leaking fault. Production wells in the QH-1 compartment produced reserves from the QH-2 compartment. The lower RD sand model helped further delineate the reservoir in the RD-2 compartment and show that this compartment has been depleted. The RD model also shows the possible presence of remaining recoverable hydrocarbons in the RD-1 compartment. It is estimated that about 6.7 billion cubic feet of gas might remain within this reservoir waiting to be recovered. A seismic amplitude anomaly response from the QH and RD sands is interpreted to be a lithologic indicator rather than the presence of hydrocarbons. Amplitude response from the PM level appears to be below the resolution of the seismic data. A synthetic seismogram model was generated to represent the PM and surrounding sands. This model shows that by increasing the frequency of the seismic data from 20 Hz to a dominant frequency of 30 Hz that the PM and surrounding sands could be seismically resolvable. Also the PM-1 compartment has possible recoverable hydrocarbons of 1.5 billion cubic feet of gas remaining.

This research examines the trade-off between the cost of adding complexity to a model and the value added to the results within the context of decision-making. It seeks to determine how complex a model should be in order to fit it to the purpose at hand. The report begins with a discussion on general modeling theory and model complexity. It next considers the specific case of petroleum reservoir models and the existing research that has compared modeling results with model complexity levels. Finally, it presents original results applying Monte Carlo sampling to a drilling decision scenario and to a one-dimensional reservoir model where a cylindrical oil field is represented by different numbers of cells and the results compared. / text

Geologic sequestration of CO2 into unmineable coal seams is proposed as a way to mitigate the greenhouse gas effect and potentially contribute to economic prosperity through enhanced methane recovery. In 2009, the Virginia Center for Coal and Energy Research (VCCER) injected 907 tonnes of CO2 into one vertical coalbed methane well for one month in Russell County, Virginia (VA). The main objective of the test was to assess storage potential of coal seams and to investigate the potential of enhanced gas recovery. In 2014, a larger scale test is planned where 20,000 tonnes of CO2 will be injected into three vertical coalbed methane wells over a period of a year in Buchanan County, VA. During primary coalbed methane production and enhanced production through CO2 injection, a series of complex physical and mechanical phenomena occur. The ability to represent the behavior of a coalbed reservoir as accurately as possible via computer simulations yields insight into the processes taking place and is an indispensable tool for the decision process of future operations. More specifically, the economic viability of projects can be assessed by predicting production: well performance can be maximized, drilling patterns can be optimized and, most importantly, associated risks with operations can be accounted for and possibly avoided. However, developing representative computer models and successfully simulating reservoir production and injection regimes is challenging. A large number of input parameters are required, many of which are uncertain even if they are determined experimentally or via in-situ measurements. Such parameters include, but are not limited to, seam geometry, formation properties, production constraints, etc. Modeling of production and injection in multi-seam formations for hydraulically fractured wells is a recent development in coalbed methane/enhanced coalbed methane (CBM/ECBM) reservoir modeling, where models become even more complex and demanding. In such cases model simulation times become important. The development of accurate simulation models that correctly account for the behavior of coalbeds in primary and enhanced production is a process that requires attention to detail, data validation, and model verification. A number of simplifying assumptions are necessary to run these models, where the user should be able to balance accuracy with computational time. In this thesis, pre- and post-injection simulations for the site in Russell County, VA, and preliminary reservoir simulations for the Buchanan County, VA, site are performed. The concepts of multi-well, multi-seam, explicitly modeled hydraulic fractures and skin factors are incorporated with field results to provide accurate modeling predictions. / Ph. D.

Reservoir heterogeneities exhibit a wide range of length scales, and their interaction with various transport mechanisms control the overall performance of subsurface flow and transport processes. Modeling these processes at large-scales requires proper scale-up of both heterogeneity and the underlying transport mechanisms. This research demonstrates a new reservoir modeling procedure to systematically quantify the scaling characteristics of transport processes by accounting for sub-scale heterogeneities and their interaction with various transport mechanisms based on the volume averaging approach. Although treatments of transport problems with the volume averaging technique have been published in the past, application to real geological systems exhibiting complex heterogeneity is lacking. A novel procedure, where results from a fine-scale numerical flow simulation reflecting the full physics of the transport process albeit over a small sub-volume of the reservoir, can be integrated with the volume averaging technique to provide effective description of transport at the coarse scale. In a volume averaging procedure, scaled up equations describing solute transport in single-phase flow are developed. Scaling characteristics of effective transport coefficient corresponding to different reservoir heterogeneity correlation lengths as well as different transport mechanisms including convection, dispersion, and diffusion are studied. The method is subsequently extended to describe transport in multiphase systems to study scaling characteristics of processes involving adsorption and inter-phase transport. Key conclusions drawn from this dissertation show that 1) variance of reservoir properties and flow responses generally decrease with scale; 2) scaling of recovery processes can be described by the scaling of effective mass transfer coefficient (Keff); in particular, mean and variance of Keff decrease with length scale, similar in the fashion of recovery statistics (e.g., variances in tracer breakthrough time and recovery); 3) the scaling of Keff depends on the underlying heterogeneity and is influenced by the dominant transport mechanisms. To show the versatility of the approach for studying scale-up of other transport mechanisms, it is also applied to derive scaled up formulations of non-Newtonian polymer flow to investigate the scaling characteristics of the apparent viscosity and effective shear rate in porous media. / text