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Joint integration of time-lapse seismic, time-lapse electromagnetic, and production data can provide a powerful means of characterizing and monitoring reservoirs. Those data contain complementary information about the changes in the reservoir during operation, and, thus, their proper integration can lead to more reliable forecasts and optimal decisions in reservoir management. This dissertation focuses on developing workflows to jointly integrate time-lapse seismic, electromagnetic, and production data because this task is significantly time-consuming and very challenging. The first part of the thesis addresses a quick and efficient method, which can provide a tool for locating the changes in the reservoir and assessing the uncertainty associated with the estimation quantitatively. The developed workflow, termed statistical integration workflow, utilizes well logs to link reservoir properties with seismic and electromagnetic data by building the joint probability distribution. A new upscaling method from well logs to the scales of seismic and electromagnetic measurements is established using multiple-point geostatistical simulation. The statistical integration workflow is applied to facies classification and the detection of depleted regions. Stochastic optimization is also investigated in this dissertation. As the joint optimization of time-lapse seismic, electromagnetic, and production data requires a huge amount of computational time, we formulate a new algorithm, the probabilistic particle swarm optimization (Pro-PSO). This algorithm is designed to alleviate the time-consuming job by parallel computations of multiple candidate models and the improvement of models based on information sharing. More importantly, any probabilistic priors, such as geological information, can be incorporated into the algorithm. Applications are investigated for a synthetic example of seismic inversion and flow history matching of a Gaussian porosity field, parameterized by its spatial principal components. The result validates the effectiveness of Pro-PSO as compared with conventional PSO. Another version of Pro-PSO for discrete parameters, called Pro-DPSO, is also developed where particles (candidate models) move in the probability mass function space instead of the parameter space. Then, Pro-DPSO is hybridized with a multiple-point geostatistical algorithm, the single normal equation algorithm (SNESIM) to preserve non-Gaussian geological features. This hybridized algorithm (Pro-DPSO-SNESIM) is evaluated on a synthetic example of seismic inversion and compared with a Markov chain Monte Carlo (MCMC) optimization method. The algorithms Pro-DPSO and Pro-DPSO-SNESIM provide not only optimized models but also optimized probability mass functions (pmf) of parameters. Therefore, it also presents the variations of realizations sampled from the optimized pmfs. Lastly, we introduce the specialization workflow of Pro-PSO algorithms for the joint integration of time-lapse seismic, time-lapse electromagnetic, and production data. In this workflow, the particles of Pro-PSO are divided into several groups, and each group is specialized in the evaluation of a particular type of data misfit. Dividing up the objective function components among different groups of particles allows the algorithm to take advantage of situations where different forward simulators for each type of data require very different computational times per iteration. The optimization is implemented by sharing multiple best models from each type of data misfit, not by sharing a single best model based on the sum (or other combinations) of all the misfits. The "divide-and-conquer" workflow is evaluated on two synthetic cases of joint integration showing that it is much more efficient than an equivalent conventional workflow minimizing an integrated objective function.
Time-lapse (4D) seismic technology is a key enabler for improved hydrocarbon recovery and more cost-effective field operations. This book shows how 4D data are used for reservoir surveillance, add value to reservoir management, and provide valuable insight on dynamic reservoir properties such as fluid saturation, pressure, and temperature.
Time-lapse seismic has evolved as an important diagnostic tool in efficient reservoir characterization and monitoring. Reservoir models, optimally constrained to seismic response, as well as flow response, can provide a better description of the reservoir and thus more reliable forecast. This dissertation focuses on different aspects of joint inversion of time-lapse seismic and production data for reservoir model updating, with application to the Norne field in the Norwegian Sea. This work describes a methodology for joint inversion of production and time-lapse seismic data, analyzes sensitive parameters in the joint inversion, identifies sensitive rock physics parameters for modeling time-lapse seismic response of a field and successfully applies and compares the family of particle swarm optimizers for joint inversion of production and time-lapse seismic data of the Norne field. The contributions from this research include a systematic workflow for joint inversion of time-lapse seismic and production data that can be and has been practically applied to a real field. Better reservoir models, constrained to both data will in turn lead to better reservoir forecasts and better field management. The first part of this thesis uses Norne field data to analyze sensitive parameters in joint inversion of production and time-lapse seismic data. An experimental design is performed on the parameters of the reservoir and seismic simulator. The results are used to rank the parameters in terms of sensitivity to production and time-lapse seismic data. At the same time it is shown that porosity/permeability models is not the most sensitive parameter for joint inversion of production and time-lapse seismic data of the Norne field. The parameters selected for study are porosity and permeability model, relative permeability, rock physics models, pore compressibility and fluid mixing. Results show that rock physics model has the most impact on time-lapse seismic whereas relative permeability is the most important parameter for production response. The results of this study are used in selecting the most important reservoir parameters for joint inversion of time-lapse seismic and production data of the Norne field. It is established that rock physics model is the most sensitive parameter for modeling time-lapse seismic of the Norne field, but there are rock physics parameters associated with rock physics model that impact time-lapse seismic modeling. So it is necessary to identify sensitive rock physics parameters for modeling time-lapse seismic response. Thus, the second part of this thesis identifies sensitive rock physics parameters in modeling time-lapse seismic response of Norne field. At first facies are classified based on well log data. Then sensitive parameters are investigated in the Gassmann's equation to generate the initial seismic velocities. The investigated parameters include mineral properties, water salinity, pore-pressure and gas-oil ratio (GOR). Next, parameter sensitivity for time-lapse seismic modeling of the Norne field is investigated. The investigated rock physics parameters are clay content, cement, pore-pressure and mixing. This sensitivity analysis helps to select important parameters for time-lapse (4D) seismic history matching which is an important aspect of joint inversion of production and time-lapse seismic of a field. Joint inversion of seismic and flow data for reservoir parameter is highly non-linear and complex. Local optimization methods may fail to obtain multiple history matched models. Recently stochastic optimization based inversion has shown very good results in the integration of time-lapse seismic and production data in reservoir history matching. Also, high dimensionality of the inverse problem makes the joint inversion of both data sets computationally expensive. High dimensionality of the inverse problem can be solved by using reduced order models. In this study, principal component bases derived from the prior is used to accomplish this. In the third part of the dissertation a family of particle swarm optimizers is used in combination with principal component bases for inversion of a synthetic data set. The performance of the different particle swarm optimizers is analyzed, both in terms of the quality of history match and convergence rate. Results show that particle swarm optimizers have very good convergence rate for a synthetic case. Also, these optimizers are used in combination with multi-dimensional scaling (MDS) to provide a set of porosity models whose simulated production and time-lapse seismic responses provide satisfactory match with the observed production and time-lapse seismic data. The goal of the last part is to apply the results of previous parts in joint inversion of production and time-lapse seismic data of the Norne field. Time-lapse seismic and production data of the Norne field is jointly inverted by varying the sensitive parameters identified in previous chapters and using different particle swarm optimizers. At first the time-lapse seismic surveys of the Norne field acquired in 2001 and 2004 is quantitatively interpreted and analyzed. Water was injected in the oil and gas producing Norne reservoir and repeat seismic surveys were conducted to monitor the subsurface fluids. The interpreted P-wave impedance change between 2001 and 2004 is used in the joint inversion loop as time-lapse seismic data. The application of different particle swarm optimizers provides a set of parameters whose simulated responses provide a satisfactory history match with the production and time-lapse seismic data of Norne field. It is shown that particle swarm optimizers have potential to be applied for joint inversion of the production and time-lapse seismic data of a real field data set.
Time-lapse (4D) seismic technology is a key enabler for improved hydrocarbon recovery and more cost-effective field operations. Practical Applications of Time-lapse Seismic Data (SEG Distinguished Instructor Series No. 16) shows how 4D seismic data are used for reservoir surveillance, how they provide valuable insight on dynamic reservoir properties such as fluid saturation, pressure, and temperature, and how they add value to reservoir management. The material, based on the 2013 SEG Distinguished Instructor Short Course, includes discussions of reservoir-engineering concepts and rock physics critical to the understanding of 4D data, along with topics in 4D seismic acquisition and processing. A primary focus of the book is interpretation and data integration. Case-study examples are used to demonstrate key concepts and are drawn on to demonstrate the range of interpretation methods currently employed by industry and the diversity of geologic settings and production scenarios in which 4D is making a difference. Time-lapse seismic interpretation is inherently integrative, drawing on geophysical, geologic, and reservoir-engineering data and concepts. As a result, this book should be of interest to individuals from all subsurface disciplines.
While geophysical reservoir characterization has been an area of research for the last three decades, geophysical reservoir monitoring, time-lapse studies, have recently become an important geophysical application. Generally speaking, the main target is to detect, estimate, and discriminate the changes in subsurface rock properties due to production. This research develops various sensitivity and feasibility analyses to investigate the effects of production-induced time-lapse changes on geophysical measurements including seismic and controlled-source electromagnetic (CSEM) data. For doing so, a realistic reservoir model is numerically simulated based on a prograding near-shore sandstone reservoir. To account for the spatial distribution of petrophysical properties, an effective porosity model is first simulated by Gaussian geostatistics. Dispersed clay and dual water models are then efficiently combined with other well-known theoretical and experimental petrophysical correlations to consistently simulate reservoir model parameters. Next, the constructed reservoir model is subjected to numerical simulation of multi-phase fluid flow to replicate a waterflooding scenario of a black oil reservoir and to predict the spatial distributions of fluid pressure and saturation. A modified Archie's equation for shaly sandstones is utilized to simulate rock resistivity. Finally, a geologically consistent stress-sensitive rock physics model, combined with the modified Gassmann theory for shaly sandstones, is utilized to simulate seismic elastic parameters. As a result, the comprehensive petro-electro-elastic model developed in this dissertation can be efficiently utilized in sensitivity and feasibility analyses of seismic/CSEM data with respect to petrophysical properties and, ultimately, applied to reservoir characterization and monitoring research. Using the resistivity models, a base and two monitor time-lapse CSEM surveys are simulated via accurate numerical algorithms. 2.5D CSEM modeling demonstrates that a detectable time-lapse signal after 5 years and a strong time-lapse signal after 10 years of waterflooding are attainable with the careful application of currently available CSEM technology. To simulate seismic waves, I employ different seismic modeling algorithms, one-dimensional (1D) acoustic and elastic ray tracing, 1D full elastic reflectivity, 2D split-step Fourier plane-wave (SFPW), and 2D stagger grid explicit finite difference (FD). My analyses demonstrate that acoustic modeling of an elastic medium is a good approximation up to ray parameter (p) equal to 0.2 sec/km. However, at p=0.3 sec/km, differences between elastic and acoustic wave propagation is the more dominant effect compared to internal multiples. Here, converted waves are also generated with significant amplitudes compared to primaries and internal multiples. I also show that time-lapse modeling of the reservoir using SFPW approach is very fast compared to FD, 100 times faster for my case here. It is capable of handling higher frequencies than FD. It provides an accurate image of the waterflooding process comparable to FD. Consequently, it is a powerful alternative for time-lapse seismic modeling. I conclude that both seismic and CSEM data have adequate but different sensitivities to changes in reservoir properties and therefore have the potential to quantitatively map production-induced time-lapse changes.
Integrated reservoir modeling has become an important part of day-to-day decision analysis in oil and gas management practices. A very attractive and promising technology is the use of time-lapse or 4D seismic as an essential component in subsurface modeling. Today, 4D seismic is enabling oil companies to optimize production and increase recovery through monitoring fluid movements throughout the reservoir. 4D seismic advances are also being driven by an increased need by the petroleum engineering community to become more quantitative and accurate in our ability to monitor reservoir processes. Qualitative interpretations of time-lapse anomalies are being replaced by quantitative inversions of 4D seismic data to produce accurate maps of fluid saturations, pore pressure, temperature, among others. Within all steps involved in this subsurface modeling process, the most demanding one is integrating the geologic model with dynamic field data, including 4Dseismic when available. The validation of the geologic model with observed dynamic data is accomplished through a "history matching" (HM) process typically carried out with well-based measurements. Due to low resolution of production data, the validation process is severely limited in its reservoir areal coverage, compromising the quality of the model and any subsequent predictive exercise. This research will aim to provide a novel history matching approach that can use information from high-resolution seismic data to supplement the areally sparse production data. The proposed approach will utilize streamline-derived sensitivities as means of relating the forward model performance with the prior geologic model. The essential ideas underlying this approach are similar to those used for high-frequency approximations in seismic wave propagation. In both cases, this leads to solutions that are defined along "streamlines" (fluid flow), or "rays" (seismic wave propagation). Synthetic and field data examples will be used extensively to demonstrate the value and contribution of this work. Our results show that the problem of non-uniqueness in this complex history matching problem is greatly reduced when constraints in the form of saturation maps from spatially closely sampled seismic data are included. Further on, our methodology can be used to quickly identify discrepancies between static and dynamic modeling. Reducing this gap will ensure robust and reliable models leading to accurate predictions and ultimately an optimum hydrocarbon extraction.
Seismic data have been established as a valuable source of information for the construction of reservoir simulation models, most commonly for determination of the modeled geologic structure, and also for population of static petrophysical properties (e.g. porosity, permeability). More recently, the availability of repeated seismic surveys over the time scale of years (i.e., 4D seismic) has shown promising results for the qualitative determination of changes in fluid phase distributions and pressure required for determination of areas of bypassed oil, swept volumes and pressure maintenance mechanisms. Quantitatively, and currently the state of the art in reservoir model characterization, 4D seismic data have proven distinctively useful for the calibration of geologic spatial variability which ultimately contributes to the improvement of reservoir development and management strategies. Among the limited variety of techniques for the integration of dynamic seismic data into reservoir models, streamline-based techniques have been demonstrated as one of the more efficient approaches as a result of their analytical sensitivity formulations. Although streamline techniques have been used in the past to integrate time-lapse seismic attributes, the applications were limited to the simplified modeling scenarios of two-phase fluid flow and invariant streamline geometry throughout the production schedule. This research builds upon and advances existing approaches to streamline-based seismic data integration for the inclusion of both production and seismic data under varying field conditions. The proposed approach integrates data from reservoirs under active reservoir management and the corresponding simulation models can be constrained using highly detailed or realistic schedules. Fundamentally, a new derivation of seismic sensitivities is proposed that is able to represent a complex reservoir evolution between consecutive seismic surveys. The approach is further extended to manage compositional reservoir simulation with dissolution effects and gravity-convective-driven flows which, in particular, are typical of CO2 transport behavior following injection into deep saline aquifers. As a final component of this research, the benefits of dynamic data integration on the determination of swept and drained volumes by injection and production, respectively, are investigated. Several synthetic and field reservoir modeling scenarios are used for an extensive demonstration of the efficacy and practical feasibility of the proposed developments.
Time-lapse seismic monitoring repeats 3D seismic imaging over a reservoir to map fluid movements in a reservoir. During hydrocarbon production, the fluid saturation, pressure, and temperature of a reservoir change, thereby altering the acoustic properties of the reservoir. Time-lapse seismic analysis can illuminate these dynamic changes of reservoir properties, and therefore has strong potential for improving reservoir management. However, the response of a reservoir depends on many parameters and can be diffcult to understand and predict. Numerical modeling results integrating streamline fluid flow simulation, rock physics, and ray-Born seismic modeling address some of these problems. Calculations show that the sensitivity of amplitude changes to porosity depend on the type of sediment comprising the reservoir. For consolidated rock, high-porosity models show larger amplitude changes than low porosity models. However, in an unconsolidated formation, there is less consistent correlation between amplitude and porosity. The rapid time-lapse modeling schemes also allow statistical analysis of the uncertainty in seismic response associated with poorly known values of reservoir parameters such as permeability and dry bulk modulus. Results show that for permeability, the maximum uncertainties in time-lapse seismic signals occur at the water front, where saturation is most variable. For the dry bulk-modulus, the uncertainty is greatest near the injection well, where the maximum saturation changes occur. Time-lapse seismic methods can also be applied to monitor CO2 sequestration. Simulations show that since the acoustic properties of CO2 are very different from those of hydrocarbons and water, it is possible to image CO2 saturation using seismic monitoring. Furthermore, amplitude changes after supercritical fluid CO2 injection are larger than liquid CO2 injection. Two seismic surveys over Teal South Field, Eugene Island, Gulf of Mexico, were acquired at different times, and the numerical models provide important insights to understand changes in the reservoir. 4D seismic differences after cross-equalization show that amplitude dimming occurs in the northeast and brightening occurs in the southwest part of the field. Our forward model, which integrates production data, petrophysicals, and seismic wave propagation simulation, shows that the amplitude dimming and brightening can be explained by pore pressure drops and gas invasion, respectively.