The topic of this thesis is streamline-based integration of dynamic data for porous media systems, particularly in petroleum reservoirs. In the petroleum industry the integration of dynamic data is usually referred to as history matching. The thesis starts out by giving an introduction to streamline-based history-matching methods. Implementations and extensions of two existing methods for streamline-based history matching are then presented. The first method pursued is based on obtaining modifications for streamline-effective properties, which subsequently are propagated to the underlying simulation grid for further iterations. For this method, two improvements are proposed to the original existing method. First, the improved approach involves less approximations, enables matching of porosity, and can account for gravity. Second, a multiscale approach is applied for which the data integration is performed on a hierarchy of coarsened grids. The approach proved robust, and gave a faster and better match to the data. The second method pursued is the so-called generalized travel-time inversion (GTTI) method, which earlier has proven very robust and efficient for history matching. The key to the efficiency of this method is the quasilinear convergence properties and the use of analytic streamline-based sensitivity coefficients. GTTI is applied together with an efficient multiscale-streamline simulator, where the pressure solver is based on a multiscale mixed finite-element method (MsMFEM). To make the history matching more efficient, a selective work-reduction strategy, based on the sensitivities provided by the inversion method, is proposed for the pressure solver. In addition, a method for improved mass conservation in streamline simulation is applied, which requires much fewer streamlines to obtain accurate production-response curves. For a reservoir model with more than one million grid blocks, 69 producers and 32 injectors, the data integration took less than twenty minutes on a standard desktop computer. Finally, we propose an extension of GTTI to fully unstructured grids, where we in particular address issues regarding regularization and computation of sensitivities on unstructured grids with large differences in cell sizes. / Paper I reprinted with kind permission of Elsevier, sciencedirect.com

Reservoir management, sometimes referred to as asset management in the context of petroleum reservoirs, has become recognized as an important facet of petroleum reservoir development and production operations. In the first stage of planning field development, the simulation model is calibrated to dynamic data (history matching). One of the aims of the research is to extend the streamline based generalized travel time inversion method for full field models with multimillion cells through the use of grid coarsening. This makes the streamline based inversion suitable for high resolution simulation models with decades long production history and numerous wells by significantly reducing the computational effort. In addition, a novel workflow is proposed to integrate well bottom-hole pressure data during model calibration and the approach is illustrated via application to the CO2 sequestration. In the second stage, field development strategies are optimized. The strategies are primarily focused on rate optimization followed by infill well drilling. A method is proposed to modify the streamline-based rate optimization approach which previously focused on maximizing sweep efficiency by equalizing arrival time of the waterfront to producers, to account for accelerated production for improving the net present value (NPV). Optimum compromise between maximizing sweep efficiency and maximizing NPV can be selected based on a 'trade-off curve.' The proposed method is demonstrated on field scale application considering geological uncertainty. Finally, a novel method for well placement optimization is proposed that relies on streamlines and time of flight to first locate the potential regions of poorly swept and drained oil. Specifically, the proposed approach utilizes a dynamic measure based on the total streamline time of flight combined with static and dynamic parameters to identify "Sweet-Spots" for infill drilling. The "Sweet-Spots" can be either used directly as potential well-placement locations or as starting points during application of a formal optimization technique. The main advantage of the proposed method is its computational efficiency in calculating dynamic measure map. The complete workflow was also demonstrated on a multimillion cell reservoir model of a mature carbonate field with notable success. The infill locations based on dynamic measure map have been verified by subsequent drilling.

In this thesis, we propose two novel approaches for hybrid Ensemble Kalman Filter (EnKF) to overcome limitations of the traditional EnKF. The first approach is to swap the ensemble mean for the ensemble mode estimation to improve the covariance calculation in EnKF. The second approach is a coarse scale permeability constraint while updating in EnKF. Both hybrid EnKF approaches are coupled with the streamline based Generalized Travel Time Inversion (GTTI) algorithm for periodic updating of the mean of the ensemble and to sequentially update the ensemble in a hybrid fashion. Through the development of the hybrid EnKF algorithm, the characteristics of the EnKF are also investigated. We found that the limits of the updated values constrain the assimilation results significantly and it is important to assess the measurement error variance to have a proper balance between preserving the prior information and the observation data misfit. Overshooting problems can be mitigated with the streamline based covariance localizations and normal score transformation of the parameters to support the Gaussian error statistics. The swapping mean and mode estimation approach can give us a better matching of the data as long as the mode solution of the inversion process is satisfactory in terms of matching the observation trajectory. The coarse scale permeability constrained hybrid approach gives us better parameter estimation in terms of capturing the main trend of the permeability field and each ensemble member is driven to the posterior mode solution from the inversion process. However the WWCT responses and pressure responses need to be captured through the inversion process to generate physically plausible coarse scale permeability data to constrain hybrid EnKF updating. Uncertainty quantification methods for EnKF were developed to verify the performance of the proposed hybrid EnKF compared to the traditional EnKF. The results show better assimilation quality through a sequence of updating and a stable solution is demonstrated. The potential of the proposed hybrid approaches are promising through the synthetic examples and a field scale application.

Integration of dynamic data is critical for reliable reservoir description and has been an outstanding challenge for the petroleum industry. This work develops practical dynamic data integration techniques using streamline approaches to condition static geological models to various kinds of dynamic data, including two-phase production history, interference pressure observations and primary production data. The proposed techniques are computationally efficient and robust, and thus well-suited for large-scale field applications. We can account for realistic field conditions, such as gravity, and changing field conditions, arising from infill drilling, pattern conversion, and recompletion, etc., during the integration of two-phase production data. Our approach is fast and exhibits rapid convergence even when the initial model is far from the solution. The power and practical applicability of the proposed techniques are demonstrated with a variety of field examples. To integrate two-phase production data, a travel-time inversion analogous to seismic inversion is adopted. We extend the method via a 'generalized travel-time' inversion to ensure matching of the entire production response rather than just a single time point while retaining most of the quasi-linear property of travel-time inversion. To integrate the interference pressure data, we propose an alternating procedure of travel-time inversion and peak amplitude inversion or pressure inversion to improve the overall matching of the pressure response. A key component of the proposed techniques is the efficient computation of the sensitivities of dynamic responses with respect to reservoir parameters. These sensitivities are calculated analytically using a single forward simulation. Thus, our methods can be orders of magnitude faster than finite-difference based numerical approaches that require multiple forward simulations. Streamline approach has also been extended to identify reservoir compartmentalization and flow barriers using primary production data in conjunction with decline type-curve analysis. The streamline 'diffusive' time of flight provides an effective way to calculate the drainage volume in 3D heterogeneous reservoirs. The flow barriers and reservoir compartmentalization are inferred based on the matching of drainage volumes from streamline-based calculation and decline type-curve analysis. The proposed approach is well-suited for application in the early stages of field development with limited well data and has been illustrated using a field example from the Gulf of Mexico.

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.

Pressure drawdown tests were conducted using Schlumberger’s Modular Formation Dynamics Tester™ (MDT) wireline tool in the Mallik methane hydrate (MH) reservoirs in February 2002 as well as in the Mount Elbert (Alaska) MH reservoirs in February 2007, while a production test was conducted applying a depressurization method in one of the Mallik MH reservoirs in April 2007. All of these tests aimed at measuring production and bottomhole pressure (BHP) responses by reducing BHP below the MH stability pressure to estimate reservoir properties such as permeability and MH dissociation radius. We attempted to analyze the results of these tests through history matching using the numerical simulator (MH21-HYDRES) coded especially for gas hydrate reservoirs. Although the magnitude of depressurization and the total duration spent for these tests were almost identical to each other, the simulation studies revealed that there existed significant differences in what could be inferred and could not be inferred from test results between a MDT test and a production test. The simulation studies mainly clarified that (1) the MDT tests were useful to estimate initial effective permeability in the presence of MH, (2) when BHP is reduced below the MH stability pressure at MDT tests, the pressure and temperature responses were significantly influenced by the wellbore storage erasing all the important data such as those indicating a radius of MH dissociation and effective permeability after partial MH dissociation, and (3) history matching of production tests tended to result in multiple solutions unless establishing steady flow conditions. This paper presents the results of history matching for the typical MDT and production tests conducted in Mallik and Alaska MH reservoirs. This paper also discusses the parameters reliably estimated through MDT and production tests, which should provide many suggestions on future designs and analyses of short-term tests for MH reservoirs.

A gas hydrate production test using the depressurization method was conducted in early April 2007 as part of the JOGMEC/NRCan/Aurora Mallik production research program. The results of the production test were analyzed using a numerical simulator (MH21-HYDRES) coded especially for gas hydrate reservoirs. This paper evaluates the test results based on analyses of production test data, numerical modeling and a series of history matching simulations. Methane gas and water was produced from a 12 m perforation interval within one of the major methane hydrate (MH) reservoirs at the Mallik MH field, by reducing the bottomhole pressure down to about 7 MPa. The measured gas production rate was far higher than that expected for a comparatively small pressure drawdown. However, irregular (on-off) pumping operations, probably related to excessive sand production, resulted in unstable fluid flow within the wellbore, which made the analysis of test performance extremely complicated. A numerical reservoir model was constructed as a series of grid blocks, including those mimicking the wellbore, to enable rigorous simulation of fluid flow patterns in the vicinity of the wellbore. The model was then tuned through history matching, not by simply adjusting reservoir parameters, but by introducing the concept that sand production might have dramatically increased the near-wellbore permeability. The good agreement between observed and simulated performances suggests the mechanism of MH dissociation/production during the test. The history matched reservoir model was employed to predict the second-year production test performance, in order to examine the gas production potential of the Mallik MH reservoir, and to provide insight into future exploration and development planning for MH reservoirs.

Unconventional reservoirs are typically characterized by very low permeabilities, and thus, the pressure depletion from a producing well may not propagate far from the well during the life of a development. Currently, two approaches are widely utilized to perform unconventional reservoir analysis: analytical techniques, including the decline curve analysis and the pressure/rate transient analysis, and numerical simulation. The numerical simulation can rigorously account for complex well geometry and reservoir heterogeneity but also is time consuming. In this thesis, we propose and apply an efficient technique, fast marching method (FMM), to analyze the shale gas reservoirs. Our proposed approach stands midway between analytic techniques and numerical simulation. In contrast to analytical techniques, it takes into account complex well geometry and reservoir heterogeneity, and it is less time consuming compared to numerical simulation. The fast marching method can efficiently provide us with the solution of the pressure front propagation equation, which can be expressed as an Eikonal equation. Our approach is based on the generalization of the concept of depth of investigation. Its application to unconventional reservoirs can provide the understanding necessary to describe and optimize the interaction between complex multi-stage fractured wells, reservoir heterogeneity, drainage volumes, pressure depletion, and well rates. The proposed method allows rapid approximation of reservoir simulation results without resorting to detailed flow simulation, and also provides the time-evolution of the well drainage volume for visualization. Calibration of reservoir models to match historical dynamic data is necessary to increase confidence in simulation models and also minimize risks in decision making. In this thesis, we propose an integrated workflow: applying the genetic algorithm (GA) to calibrate the model parameters, and utilizing the fast marching based approach for forward simulation. This workflow takes advantages of both the derivative free characteristics of GA and the speed of FMM. In addition, we also provide a novel approach to incorporate the micro-seismic events (if available) into our history matching workflow so as to further constrain and better calibrate our models.

No description available.