Time-lapse seismic monitoring is a powerful technique for reservoir management and the optimization of hydrocarbon recovery. In time-lapse seismic datasets, the difference in seismic properties across different vintages enables the detection of spatio-temporal changes in saturated properties and structure induced by production. The main objectives are (1) to identify bypass pay zones in time-lapse seismic data for the deepwater Amberjack field, located in the Gulf of Mexico, (2) confirm the identified bypass pay zones in the results of reservoir simulation, and (3) recommend well planning strategies to exploit these bypassed resources.
A high-fidelity seismic-to-simulation 4D workflow that incorporates seismic, petrophysics, petrophysical property modeling, and reservoir simulation was employed, which leveraged cross-discipline interaction, interpretation, and integration to extend asset management capabilities. The workflow addresses geology (well log interpretation and framework development), geophysics (seismic interpretation, velocity modeling, and seismic inversion), and petrophysical property modeling (earth models and co-located co-simulation of petrophysical properties with P-impedance from seismic inversion). An embedded petro-elastic model (PEM) in the reservoir simulator is then used to affiliate spatial dry rock properties with saturation properties to compute dynamic elastic properties, which can be related to multi-vintage P-impedance from time-lapse seismic inversion. In the absence of the requisite dry rock properties for the PEM, a small data engine is used to determine these absent properties using metaheuristic optimization techniques. Specifically, two particle swarm optimization (PSO) applications, including an exterior penalty function (EPF), are modified resulting in the development of nested and average methods, respectively. These methods simultaneously calculate the missing rock parameters (dry rock bulk modulus, shear modulus, and density) necessary for dynamic, embedded P-impedance calculation in the history-constrained reservoir simulation results. Afterward, a graphic-enabled method was devised to appropriately classify the threshold to discriminate non-reservoir (including bypassed pay) and reservoir from the P-impedance difference. Its results are compared to unsupervised learning (k-means clustering and hierarchical clustering). From seismic data, one can identify bypassed pay locations, which are confirmed from reservoir simulation after conducting a seismic-driven history match. Finally, infill wells are planned, and then modeled in the reservoir simulator.
Azevedo, Leonardo (Cerena/Decivil, Instituto Superior Técnico) | Demyanov, Vasily (Institute of Petroleum Engineering, Heriot-Watt University) | Lopes, Diogo (Cerena/Decivil, Instituto Superior Técnico) | Soares, Amílcar (Cerena/Decivil, Instituto Superior Técnico) | Guerreiro, Luis (Partex Oil & Gas)
Geostatistical seismic inversion uses stochastic sequential simulation and co-simulation as the perturbation techniques to generate and perturb elastic models. These inversion methods allow retrieve high-resolution inverse models and assess the spatial uncertainty of the inverted properties. However, they assume a given number of a priori parametrization often considered known and certain, which is exactly reproduce in the final inverted models. This is the case of the top and base of main seismic units to which regional variogram models and histrograms are assigned. Nevertheless, the amount of existing well-log data (i.e., direct measurements) of the property to be inverted if often not enough to model variograms and its histograms are biased towards the more sand-prone facies. This work shows a consistent stochastic framework that allows to quantify uncertainties on these parameters which are associated with large-scale geological features. We couple stochastic adaptive sampling (i.e., particle swarm optimization) with global stochastic inversion to infer three-dimensional acoustic impedance from existing seismic reflection data. Key uncertain geological parameters are first identified, and reliable a priori distributions inferred from geological knowledge are assigned to each parameter. The type and shape of each distribution reflects the level of knowledge about this parameter. Then, particle swarm optimization is integrated as part of an iterative geostatistical seismic inversion methodology and these parameters are optimized along with the spatial distribution of acoustic impedance. At the end of the iterative procedure, we retrieve the best-fit inverse model of acoustic impedance along with the most probable value for the location of top and base of each seismic unit, the most likely histogram and variogram model per zone. We couple stochastic adaptive sampling (i.e., particle swarm optimization) with global stochastic inversion to infer three-dimensional acoustic impedance from existing seismic reflection data. Key uncertain geological parameters are first identified, and reliable a priori distributions of potential values are assigned to each parameter. The type and shape of each distribution reflects the level of knowledge about this parameter. Then, particle swarm optimization is integrated as part of an iterative geostatistical seismic inversion methodology and these parameters are optimized along with the spatial distribution of acoustic impedance. At the end of the iterative procedure we retrieve the best-fit inverse model of acoustic impedance along with the most probable value for the location of top and base of each seismic unit, the most likely histogram and variogram model per zone.
Modern seismic data surveys generate terabytes of data daily leading to a significant increase of the cost for storage and transmission. Therefore, it is desired to compress seismic data. In this work, we propose a model-based compression scheme to deal with the large data volume. First, each seismic trace is modeled as a superposition of multiple exponentially decaying sinusoidal waves (EDSWs). Each EDSW represents a model component and is defined by a set of parameters. Secondly, a parameter estimation algorithm for this model is proposed using Particle Swarm Optimization (PSO) technique. In the proposed algorithm, the parameters of each EDSW are estimated sequentially wave by wave. A suitable number of model components for each trace is determined according to the level of the residuals energy. The proposed model based compression scheme is then experimentally compared with the discrete Cosine transform (DCT) on a real seismic data. The proposed model based algorithm outperforms the DCT in term of compression ratio and reconstruction quality.
Presentation Date: Tuesday, October 16, 2018
Start Time: 1:50:00 PM
Location: Poster Station 20
Presentation Type: Poster
The extraction of seismic wavelet phase plays an important role in deconvolution and high resolution processing technique. In order to obtain the accurate phase of mixed-phase wavelet, this paper proposed a phase extraction method which combines bispectrum estimation algorithm with parameters optimization of phase only filter. First, the bispectrum of seismic records is used for phase estimation. Then, parameters of the phase only filter can be restricted based on bispectrum estimation results. Finally, the precise phase of wavelet is obtained by particle swarm optimization which can seek accurate parameters of the phase only filter. The simulation and comparison results show that the proposed method has effectiveness and is more efficient than conventional phase only filter method because parameters are limited by bispectrum estimation.
Presentation Date: Wednesday, September 27, 2017
Start Time: 4:20 PM
Presentation Type: ORAL
Zhang, Bing (Jilin University) | Liu, Cai (Jilin University) | Guo, Zhiqi (Jilin University) | Lu, Neng (Jilin University) | Liu, Xiwu (Sinopec and National Key Laboratory of Corporation of Shale Oil/Gas Enrichment Mechanism and Effective Development)
A stochastic inversion method of reservoir properties for anisotropic shales is proposed by combing rock physics model and Bayesian estimation. Quantitative relations between elastic parameter such as P- and S-wave impedances and reservoir properties including fracture and porosity are investigated using the statistical rock physics model. During the modeling, the error between the rock physics model and reservoirs, as well as noises in the seismic data are considered. For the process of estimating reservoir properties from elastic parameters, Bayesian inversion based on statistical rock physics model is applicable to the uncertainty problem by computing the posterior probability distribution (PDF) of the reservoir properties. Based on rock physics modeling and given prior knowledge of the reservoir, reservoir properties are obtained by the maximum a posteriori (MAP) criterion and associated uncertainty analysis. In the stochastic inversion, the SA-PSO algorithm which combines the simulated annealing method and the particle swarm optimization method shows its advantages in accuracy and efficiency. The estimated reservoir properties can be used for better characterizations of the sweet spots in shale reservoirs.
This paper has been withdrawn from the Technical Program and will not be presented at the 87th SEG Annual Meeting.
Dou, Shan (Lawrence Berkeley National Laboratory) | Ajo-Franklin, Jonathan (Lawrence Berkeley National Laboratory) | Dafflon, Baptiste (Lawrence Berkeley National Laboratory) | Peterson, John (Lawrence Berkeley National Laboratory) | Ulrich, Craig (Lawrence Berkeley National Laboratory) | Hubbard, Susan (Lawrence Berkeley National Laboratory) | Dreger, Douglas (University of California–Berkeley)
Surface waves are effective in identifying inversely dispersive media, but because dispersion-curve retrieval is susceptible to ambiguities, commonly-used inversion methods often become inapplicable. Here we highlight a full-wavefield approach that uses dispersion spectra instead of dispersion curves to invert for shear-wave velocity (
Presentation Date: Monday, September 25, 2017
Start Time: 1:50 PM
Presentation Type: ORAL
Olalekan, Fayemi (Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Beijing 100029, China) | Di, Qingyun (Key Laboratory of Shale Gas and Geoengineering, Institute of Geology and Geophysics, Beijing 100029, China)
This study introduces the application of an improved implementation work flow for centered-centered progressive PSO (IRCCPSO) inversion technique for Multi-transient electromagnetic method (MTEM) full waveform inversion. The stabilizing functional was used to introduce the constraint in the inversion algorithm; thus, the global best position was updated using multi-objective functional. Firstly, 1D study using conventional IRCCPSO technique was presented. Furthermore, 2D inversion study over a buried resistive body model was carried out using a limited search space. The obtained inversion results were good representation of the earth model. Consequently, this confirms the effectiveness of the IRCCPSO technique as a good geophysical tool for MTEM full waveform inversion.
Presentation Date: Wednesday, September 27, 2017
Start Time: 2:40 PM
Location: Exhibit Hall C, E-P Station 2
Presentation Type: EPOSTER
In this paper we propose a proxy model based seismic history matching (SHM), and apply it to time-lapse (4D) seismic data from a Norwegian Sea field. A stable proxy model is developed for generating 4D seismic attributes by using only the original baseline seismic data and dynamic pressure and saturation predictions from reservoir flow simulation. This method (
In this study we firstly perform a check on the validity and accuracy of the proxy approach following the methodology of (
The elastic parameters (
Presentation Date: Tuesday, October 18, 2016
Start Time: 8:25:00 AM
Location: Lobby D/C
Presentation Type: POSTER
Inversion for seismic impedance is an ill-posed and nonlinear problem. Hence inversion results are non-unique and unstable. Scholars have made great efforts in this research and recent years it has emerged more and more new non-linear inversion method with the application of the nonlinear inversion problems. Standard particle swarm optimization (PSO) is not appropriate when we use it for the post-stack impedance directly. So we come up with an improved particle swarm optimization to alleviate these problems for the post-stack impedance inversion. This improved particle swarm optimization is based on the swarm intelligence and probabilistic theory for global optimization. This paper applied this method in the observation data of post-stack impedance inversion. The results show that this improved particle swarm optimization algorithm is a global optimization algorithm with a better performance than standard PSO for post-stack impedance inversion. It is feasible and effective for impedance inversion problem.
Presentation Date: Monday, October 17, 2016
Start Time: 4:10:00 PM
Location: Lobby D/C
Presentation Type: POSTER