Browsing by Subject "Reservoir Characterization"
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Item A Hybrid Ensemble Kalman Filter for Nonlinear Dynamics(2011-02-22) Watanabe, ShingoIn 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.Item Applications of Level Set and Fast Marching Methods in Reservoir Characterization(2012-10-19) Xie, JiangReservoir characterization is one of the most important problems in petroleum engineering. It involves forward reservoir modeling that predicts the fluid behavior in the reservoir and inverse problem that calibrates created reservoir models with given data. In this dissertation, we focus on two problems in the field of reservoir characterization: depth of investigation in heterogeneous reservoirs, and history matching and uncertainty quantification of channelized reservoirs. The concept of depth of investigation is fundamental to well test analysis. Much of the current well test analysis relies on analytical solutions based on homogeneous or layered reservoirs. However, such analytic solutions are severely limited for heterogeneous and fractured reservoirs, particularly for unconventional reservoirs with multistage hydraulic fractures. We first generalize the concept to heterogeneous reservoirs and provide an efficient tool to calculate drainage volume using fast marching methods and estimate pressure depletion based on geometric pressure approximation. The applicability of proposed method is illustrated using two applications in unconventional reservoirs including flow regime visualization and stimulated reservoir volume estimation. Due to high permeability contrast and non-Gaussianity of channelized permeability field, it is difficult to history match and quantify uncertainty of channelized reservoirs using traditional approaches. We treat facies boundaries as level set functions and solve the moving boundary problem (history matching) with the level set equation. In addition to level set methods, we also exploit the problem using pixel based approach. The reversible jump Markov Chain Monte Carlo approach is utilized to search the parameter space with flexible dimensions. Both proposed approaches are demonstrated with two and three dimensional examples.Item Fast history matching of finite-difference model, compressible and three-phase flow using streamline-derived sensitivities(Texas A&M University, 2006-10-30) Cheng, HaoReconciling 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.Item Heterogeneous Reservoir Characterization Utilizing Efficient Geology Preserving Reservoir Parameterization through Higher Order Singular Value Decomposition (HOSVD)(2015-01-21) Afra, SardarPetroleum reservoir parameter inference is a challenging problem to many of the reservoir simulation work flows, especially when it comes to real reservoirs with high degree of complexity and non-linearity, and high dimensionality. In fact, the process of estimating a large number of unknowns in an inverse problem lead to a very costly computational effort. Moreover, it is very important to perform geologically consistent reservoir parameter adjustments as data is being assimilated in the history matching process, i.e., the process of adjusting the parameters of reservoir system in order to match the output of the reservoir model with the previous reservoir production data. As a matter of fact, it is of great interest to approximate reservoir petrophysical properties like permeability and porosity while reparameterizing these parameters through reduced-order models. As we will show, petroleum reservoir models are commonly described by in general complex, nonlinear, and large-scale, i.e., large number of states and unknown parameters. Thus, having a practical approach to reduce the number of reservoir parameters in order to reconstruct the reservoir model with a lower dimensionality is of high interest. Furthermore, de-correlating system parameters in all history matching and reservoir characterization problems keeping the geological description intact is paramount to control the ill-posedness of the system. In the first part of the present work, we will introduce the advantages of a novel parameterization method by means of higher order singular value decomposition analysis (HOSVD). We will show that HOSVD outperforms classical parameterization techniques with respect to computational and implementation cost. It also, provides more reliable and accurate predictions in the petroleum reservoir history matching problem due to its capability to preserve geological features of the reservoir parameter like permeability. The promising power of HOSVD is investigated through several synthetic and real petroleum reservoir benchmarks and all results are compared to that of classic SVD. In addition to the parameterization problem, we also addressed the ability of HOSVD in producing accurate production data comparing to those of original reservoir system. To generate the results of the present work, we employ a commercial reservoir simulator known as ECLIPSE. In the second part of the work, we will address the inverse modeling, i.e., the reservoir history matching problem. We employed the ensemble Kalman filter (EnKF) which is an ensemble-based characterization approach to solve the inverse problem. We also, integrate our new parameterization technique into the EnKF algorithm to study the suitability of HOSVD based parameterization for reducing the dimensionality of parameter space and for estimating geologically consistence permeability distributions. The results of the present work illustrates the characteristics of the proposed parameterization method by several numerical examples in the second part including synthetic and real reservoir benchmarks. Moreover, the HOSVD advantages are discussed by comparing its performance to the classic SVD (PCA) parameterization approach. In the first part of the present work, we will introduce the advantages of a novel parameterization method by means of higher order singular value decomposition analysis (HOSVD). We will show that HOSVD outperforms classical parameterization techniques with respect to computational and implementation cost. It also, provides more reliable and accurate predictions in the petroleum reservoir history matching problem due to its capability to preserve geological features of the reservoir parameter like permeability. The promising power of HOSVD is investigated through several synthetic and real petroleum reservoir benchmarks and all results are compared to that of classic SVD. In addition to the parameterization problem, we also addressed the ability of HOSVD in producing accurate production data comparing to those of original reservoir system. To generate the results of the present work, we employ a commercial reservoir simulator known as ECLIPSE. In the second part of the work, we will address the inverse modeling, i.e., the reservoir history matching problem. We employed the ensemble Kalman filter (EnKF) which is an ensemble-based characterization approach to solve the inverse problem. We also, integrate our new parameterization technique into the EnKF algorithm to study the suitability of HOSVD based parameterization for reducing the dimensionality of parameter space and for estimating geologically consistence permeability distributions. The results of the present work illustrate the characteristics of the proposed parameterization method by several numerical examples in the second part including synthetic and real reservoir benchmarks. Moreover, the HOSVD advantages are discussed by comparing its performance to the classic SVD (PCA) parameterization approach.Item High resolution sequence stratigraphic and reservoir characterization studies of D-07, D-08 and E-01 sands, Block 2 Meren field, offshore Niger Delta(Texas A&M University, 2004-09-30) Esan, Adegbenga OluwafemiMeren field, located offshore Niger Delta, is one of the most prolific oil-producing fields in the Niger Delta. The upper Miocene D-07, D-08 and E-01 oil sands comprise a series of stacked hydrocarbon reservoirs in Block 2 of Meren field. These reservoir sandstones were deposited in offshore to upper shoreface environments. Seven depositional facies were identified in the studied interval, each with distinct lithology, sedimentary structures, trace fossils, and wire-line log character. The dominant lithofacies are (1) locally calcite-cemented highly-bioturbated, fine-grained sandstones, (middle to lower shoreface facies); (2) cross-bedded, fine- to medium-grained well-sorted sandstones (upper shoreface facies); (3) horizontal to sub-horizontal laminated, very-fine- to fine-grained sandstone (delta front facies); (4) massive very-fine- to fine-grained poorly-sorted sandstone (delta front facies); (5) muddy silt- to fine-grained wavy-bedded sandstone (lower shoreface facies); (6) very-fine- to fine-grained sandy mudstone (lower shoreface facies); and (7) massive, silty shales (offshore marine facies). Lithofacies have distinct mean petrophysical properties, although there is overlap in the range of values. The highest quality reservoir deposits are cross-bedded sands that were deposited in high-energy upper shoreface environments. Calcite cements in lower shoreface facies significantly reduce porosity and permeability. Integration of core and wire-line log data allowed porosity and permeability to be empirically determined from bulk density. The derived equation indicated that bulk density values could predict 80% of the variance in core porosity and permeability values. Three parasequence sets were interpreted, including one lower progradational and two upper retrogradational parasequence sets. The progradational parasequence set consists of upward-coarsening delta front to upper shoreface facies, whereas the upward-fining retrogradational parasequence sets are composed of middle to lower shoreface deposits overlain by offshore marine shales. The limited amount of core data and the relatively small area of investigation place serious constraints on stratigraphic interpretations. Two possible sequence stratigraphic interpretations are presented. The first interpretation suggests the deposits comprise a highstand systems tract overlain by a transgressive systems tract. A lowstand systems tract is restricted to an incised valley fill at the southeastern end of the study area. The alternate interpretation suggests the deposits comprise a falling stage systems tract overlain by transgressive systems tract.Item Interpreting Horizontal Well Flow Profiles and Optimizing Well Performance by Downhole Temperature and Pressure Data(2011-02-22) Li, ZhuoyiHorizontal well temperature and pressure distributions can be measured by production logging or downhole permanent sensors, such as fiber optic distributed temperature sensors (DTS). Correct interpretation of temperature and pressure data can be used to obtain downhole flow conditions, which is key information to control and optimize horizontal well production. However, the fluid flow in the reservoir is often multiphase and complex, which makes temperature and pressure interpretation very difficult. In addition, the continuous measurement provides transient temperature behavior which increases the complexity of the problem. To interpret these measured data correctly, a comprehensive model is required. In this study, an interpretation model is developed to predict flow profile of a horizontal well from downhole temperature and pressure measurement. The model consists of a wellbore model and a reservoir model. The reservoir model can handle transient, multiphase flow and it includes a flow model and a thermal model. The calculation of the reservoir flow model is based on the streamline simulation and the calculation of reservoir thermal model is based on the finite difference method. The reservoir thermal model includes thermal expansion and viscous dissipation heating which can reflect small temperature changes caused by pressure difference. We combine the reservoir model with a horizontal well flow and temperature model as the forward model. Based on this forward model, by making the forward calculated temperature and pressure match the observed data, we can inverse temperature and pressure data to downhole flow rate profiles. Two commonly used inversion methods, Levenberg- Marquardt method and Marcov chain Monte Carlo method, are discussed in the study. Field applications illustrate the feasibility of using this model to interpret the field measured data and assist production optimization. The reservoir model also reveals the relationship between temperature behavior and reservoir permeability characteristic. The measured temperature information can help us to characterize a reservoir when the reservoir modeling is done only with limited information. The transient temperature information can be used in horizontal well optimization by controlling the flow rate until favorite temperature distribution is achieved. With temperature feedback and inflow control valves (ICVs), we developed a procedure of using DTS data to optimize horizontal well performance. The synthetic examples show that this method is useful at a certain level of temperature resolution and data noise.Item Joint Inversion of Production and Temperature Data Illuminates Vertical Permeability Distribution in Deep Reservoirs(2012-10-19) Zhang, ZhishuaiCharacterization of connectivity in compartmentalized deepwater Gulf of Mexico (GoM) reservoirs is an outstanding challenge of the industry that can significantly impact the development planning and recovery from these assets. In these deep formations, temperature gradient can be quite significant and temperature data can provide valuable information about field connectivity, vertical fluid displacement, and permeability distribution in the vertical direction. In this paper, we examine the importance of temperature data by integrating production and temperature data jointly and individually and conclude that including the temperature data in history matching of deep GoM reservoirs can increase the resolution of reservoir permeability distribution map in the vertical direction. To illustrate the importance of temperature measurements, we use a coupled heat and fluid flow transport model to predict the heat and fluid transport in the reservoir. Using this model we ran a series of data integration studies including: 1) integration of production data alone, 2) integration of temperature data alone, and 3) joint integration of production and temperature data. For data integration, we applied four algorithms: Maximum A-Posteriori (MAP), Randomized Maximum Likelihood (RML), Sparsity Regularized Reconstruction and Sparsity Regularized RML methods. The RML and Sparsity Regularized RML approaches were used because they allow for uncertainty quantification and estimation of reservoir heterogeneity at a higher resolution. We also investigated the sensitivity of temperature and production data to the distribution of permeability, which showed that while production data primarily resolved the distribution of permeability in the horizontal direction, the temperature data did not display much sensitivity to permeability in the horizontal extent of the reservoir. The results of these experiments were compelling in that they clearly illuminated the role of temperature data in enhancing the resolution of reservoir permeability maps with depth. We present several experiments that clearly illustrate and support the conclusions of this study.Item Reservoir characterization using experimental design and response surface methodology(Texas A&M University, 2004-09-30) Parikh, HarshalThis research combines a statistical tool called experimental design/response surface methodology with reservoir modeling and flow simulation for the purpose of reservoir characterization. Very often, it requires large number of reservoir simulation runs for identifying significant reservoir modeling parameters impacting flow response and for history matching. Experimental design/response surface (ED/RS) is a statistical technique, which allows a systematic approach for minimizing the number of simulation runs to meet the two objectives mentioned above. This methodology may be applied to synthetic and field cases using existing statistical software tools. The application of ED/RS methodology for the purpose of reservoir characterization has been applied for two different objectives. The first objective is to address the uncertainties in the identification of the location and transmissibility of flow barriers in a field in the Gulf of Mexico. This objective is achieved by setting up a simple full-factorial design. The range of transmissibility of the barriers is selected using a Latin Hypercube Sampling (LHS). An analysis of variance (ANOVA) gives the significance of the location and transmissibility of barriers and comparison with decline-type curve analysis which gives us the most likely scenarios of the location and transmissibility of the flow barriers. The second objective is to identify significant geologic parameters in object-based and pixel-based reservoir models. This study is applied on a synthetic fluvial reservoir, whose characteristic feature is the presence of sinuous sand filled channels within a background of floodplain shale. This particular study reveals the impact of uncertainty in the reservoir modeling parameters on the flow performance. Box-Behnken design is used in this study to reduce the number of simulation runs along with streamline simulation for flow modeling purposes. In the first study, we find a good match between field data and that predicted from streamline simulation based on the most likely scenario. This validates the use of ED to get the most likely scenario for the location and transmissibility of flow barriers. It can be concluded from the second study that ED/RS methodology is a powerful tool along with a fast streamline simulator to screen large number of reservoir model realizations for the purpose of studying the effect of uncertainty of geologic modeling parameters on reservoir flow behavior.Item Streamline Assisted Ensemble Kalman Filter - Formulation and Field Application(2010-10-12) Devegowda, DeepakThe goal of any data assimilation or history matching algorithm is to enable better reservoir management decisions through the construction of reliable reservoir performance models and the assessment of the underlying uncertainties. A considerable body of research work and enhanced computational capabilities have led to an increased application of robust and efficient history matching algorithms to condition reservoir models to dynamic data. Moreover, there has been a shift towards generating multiple plausible reservoir models in recognition of the significance of the associated uncertainties. This provides for uncertainty analysis in reservoir performance forecasts, enabling better management decisions for reservoir development. Additionally, the increased deployment of permanent well sensors and downhole monitors has led to an increasing interest in maintaining 'live' models that are current and consistent with historical observations. One such data assimilation approach that has gained popularity in the recent past is the Ensemble Kalman Filter (EnKF) (Evensen 2003). It is a Monte Carlo approach to generate a suite of plausible subsurface models conditioned to previously obtained measurements. One advantage of the EnKF is its ability to integrate different types of data at different scales thereby allowing for a framework where all available dynamic data is simultaneously or sequentially utilized to improve estimates of the reservoir model parameters. Of particular interest is the use of partitioning tracer data to infer the location and distribution of target un-swept oil. Due to the difficulty in differentiating the relative effects of spatial variations in fractional flow and fluid saturations and partitioning coefficients on the tracer response, interpretation of partitioning tracer responses is particularly challenging in the presence of mobile oil saturations. The purpose of this research is to improve the performance of the EnKF in parameter estimation for reservoir characterization studies without the use of a large ensemble size so as to keep the algorithm efficient and computationally inexpensive for large, field-scale models. To achieve this, we propose the use of streamline-derived information to mitigate problems associated with the use of the EnKF with small sample sizes and non-linear dynamics in non-Gaussian settings. Following this, we present the application of the EnKF for interpretation of partitioning tracer tests specifically to obtain improved estimates of the spatial distribution of target oil.