Geophysical Joint Inversion
Oil Geophysical Prospecting,2018,Vol 53,No. 03
【Abstract】 A single set of sonic velocity or resistivity data are usually used to estimate hydrate reservoir parameters based on simple linear or logarithmic linear rock physics. However, elastic data and electrical data have different sensitivity of reservoir parameters; the estimation based on different data may generate different values of reservoir parameters. In order to solve this problem, we propose a new method of elastic-electrical joint inversion based on a nonlinear simplified three-phrase equation and a modified Archie equation under the frame of Bayesian theory, with which we can simultaneously predict gas hydrate saturation, porosity, clay content, and their corresponding uncertainty. The feasibility of the joint inversion method is verified by both synthetic data tests and real well logging data tests. The results indicate that the joint inversion of elastic-electrical data can not only produce reliable reservoir parameter estimations, but also reduce effectively the uncertainty caused by different sensitivities and noises.
Oil Geophysical Prospecting,2018,Vol 53,No. 05
【Abstract】 Seismic survey is the major approach to oil and gas exploration and development, but the ambiguity exists during estimating the reservoir petrophysical parameters such as hydrocarbon saturation only with the seismic data. Marine controlled-source electromagnetic (MCSEM) data is sensitive to resistive hydrocarbon reservoirs and thus aids seismic survey to increase the well drilling success rate. Therefore, the joint inversion of MCSEM and seismic data contributes to more reliable estimation of reservoir petrophysical parameters. This paper proposes an approach to estimate petrophysical parameters with MCSEM and seismic amplitude variation with angle of incidence (AVA) data. The approach utilizes Archie’s equation and Gassmann’s equations to relate reservoir petrophysical parameters to the conductivity and seismic wave velocities as well as density, and adopts the simulated annealing (SA) optimization algorithm to minimize the joint inversion objective function. The numerical experiments show that the joint inversion can estimate more reliable reservoir petrophysical parameters compared with only MCSEM or seismic AVA data inversion. Compared to the Occam method based on descent gradient, this inversion is independent from an initial guess.
Oil Geophysical Prospecting,2017,Vol 52,No. 03
【Abstract】 1D forward modeling of marine electromagnetic (MCSEM) data shows that both frequency-domain data and time-domain data have certain limitations when using a horizontal electric dipole as the emission source in shallow water. In order to better use MCSEM for offshore oil and gas exploration, we acquire frequency-domain and time-domain data at the same time based on the idea of the time-frequency electromagnetic (TFEM) method used on land. We achieve 1D joint inversion of time-domain and frequency-domain electromagnetic data with the regularization inversion method. According to our model data tests, for a deep reservoir model with 300 m water depth, inverted frequency-domain data can show reservoir location and resistivity, but additional time-domain data cannot improve resolution; for a deep reservoir model with 100 m water depth, the joint inversion results are better than that of a single domain data set.
Oil Geophysical Prospecting,2017,Vol 52,No. 04
【Abstract】 For previous geophysical joint inversion research, geophysicists’ concerns usually are some key techniques rather than its framework. In addition, an initial model building for rigorous joint inversions is strongly dependent on artificial judgments, and the results considerably rely on the initial model. Therefore, we provided a new framework for geophysical joint inversion, which was called as “multiple modeling, integrated constraints, and step-by-step inversion”. In this framework, model buildings and joint inversions were realized in stages based on multiple types of constraints. We took a joint inversion of magnetotelluric (MT) and seismic data for example to illustrate the new framework, and designed a typical basalt model to test the new framework. MT and seismic methods showed their limitations in the single model test. Then, we applied the joint inversion to complement each other based on the new framework, and studied the distribution of strata under basalt using a common initial model. Comparing with the formerly joint inversion needing a detailed initial model, the new method could reduce the influence of human factors during the initial model building, and lower the reliance of the result on the initial model to some extent.
Joint inversion of PP- and PS-waves based on the iteration of ratio of S-wave velocity and P-wave velocity
Oil Geophysical Prospecting,2016,Vol 51,No. 01
【Abstract】 If we do not have velocity information when extracting P-wave velocity, S-wave velocity, and density from EI (elastic impedance), k (ratio of S-wave velocity and P-wave velocity) is set to a constant, which is always different from real k. This assumption leads to errors in the inversion results. It is found that inversion of P-wave velocity, S-wave velocity, and density based on EI is unstable due to noise (lack of information of big incident angles). We analyze influences of k on inversion results by the parameter resolution matrix, and have found that P-wave velocity and density are hardly affected by k, and that S-wave velocity is very sensitive to k. Based on this point, we design a nonlinear k value iteration method to obtain first accurate k value, and then accurate S-wave velocity. Then we derive P-SV wave elastic impedance accurate (SEI) formula based on Aki-Richards P-SV wave reflection coefficient approximation formula. Finally we propose a joint iteration of k value to extract P-wave velocity, S-wave velocity, and density from EI and SEI. The proposed join inversion is tested on a four-layer model and Marmousi 2 model. The tests results show that k value can rapidly converge to its real value and inversed P-wave velocity, S-wave velocity and density are more accurate and stable.
Oil Geophysical Prospecting,2016,Vol 51,No. 06
【Abstract】 Joint inversion of multi-scale geophysical datasets is an effective way to improve accuracy and resolution of seismic inversion. Considering different confidences of data in different scales, we propose a sparse Bayesian joint inversion method of multi-scale seismic data. Firstly we obtain scale constrained operator for different scale data through matching analysis for well-side synthetic seismogram with surface seismic data. Based on the Bayesian inversion framework, assuming that model parameters obey Cauchy prior distribution to retreive the sparse results, we derive a cost function for sparse Bayesian joint inversion for multiscale seismic datasets with scale-constrained operator, and employ Polak-Ribiere-Polyak (PRP) conjugate gradient algorithm to solve the optimization problem. Model and real data tests show that the proposed inversion can highlight reservoir information from surface seismic data and borehole seismic data in high-confidence scale, and provide the high accuracy inversion results for reservoir-oriented integrated investigation.