Download Free Detection Of Production Induced Time Lapse Signatures By Geophysical Seismic And Csem Measurements Book in PDF and EPUB Free Download. You can read online Detection Of Production Induced Time Lapse Signatures By Geophysical Seismic And Csem Measurements and write the review.

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.
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 (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.
Presents a collection of papers which appear in the September-October 2010 Geophysics special section, written by recognised experts in various areas of exploration geophysics, plus an additional group of papers drawn from Geophysics which address areas beyond those invited articles. The result is a snapshot of the state-of-the-art in the field.
Compaction in reservoir overburden can impact production facilities and lead to a significant risk of well-bore failures. Prevalent practice of time-lapse seismic processing of 4D data above compacting reservoirs relies on picking time displacements and converting them into estimated velocity changes and subsurface deformation. This approach relies on prior data equalization, and requires a significant amount of manual interpretation and quality control. In this thesis I develop methods for automatic detection of production-induced subsurface velocity changes from seismic data, and computational techniques of subsurface characterization from measurable surface deformation. In the first part of my work I describe a time-lapse inversion technique based on a simultaneous regularized full-waveform inversion of multiple surveys. I provide a theoretical foundation of the proposed method, and analyze its sensitivity to a realistic 1-2% velocity deformation in the overburden. The method is applied in a study of overburden dilation above the Gulf of Mexico Genesis field and achieves a stable recovery of "blocky" negative velocity anomalies above compacting reservoirs. I propose a multi-scale extension of the method for recovering both long and short-wavelength velocity changes. In the second part I describe a geomechanical model of overburden deformation in response to fluid extraction or injection. I provide a method for inverting pore pressure changes from noisy and sparse measurements of surface deformation. The method is applied to estimating the efficiency of Cyclic Steam Stimulation (CSS) of a heavy oil reservoir. Inverted subsurface pore pressure changes indicate a significant heterogeneity of the propagating steam fronts. The method is extended to a study of sharp contrasts in reservoir attributes by using a new splitting algorithm for solving large-scale constrained regularized optimization problems.
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.