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The traditional algorithm for transient electromagnetic (TEM) method inversion employs “smoke ring” imaging, which can only reflect the approximate morphology of the stratigraphic model and low precision. In this paper, we apply the firefly algorithm (FA) technology for TEM inversion by comparing the inversion with a thermodynamic process. The results show that the firefly algorithm has a higher degree of inversion fitting to the model. We examine the effectiveness of the algorithm for TEM by inverting both theoretical and survey data and by comparing the results with those from the “smoke ring” and PSO algorithms. Note: This paperÂ wasÂ acceptedÂ into the Technical Program but was not presented at the 2020 SEG Annual Meeting.
We present the modeling, acquisition, processing, and results from a lightweight surface microseismic monitoring array using 630 total geophones. We show that through careful planning, design and processing, it is possible to extract a high-quality microseismic data set from minimum acquisition. We located over 3700 events with down to a magnitude of −1.79. The processed data set provided an interpretable catalog including moment tensors to help evaluate the performance of the fracture treatment. This procedure can provide a model of how to optimize monitoring budgets to deliver results for future projects. Presentation Date: Wednesday, October 14, 2020 Session Start Time: 1:50 PM Presentation Time: 1:50 PM Location: 360C Presentation Type: Oral
Cai, Hanpeng (Center for information geoscience & School of Resources and Environment, UESTC) | Qin, Qing (Center for information geoscience & School of Resources and Environment, UESTC) | Li, Huiqiang (Center for information geoscience & School of Resources and Environment, UESTC) | Wang, Yaojun (Center for information geoscience & School of Resources and Environment, UESTC) | Zhang, Yuejing (Research Institute of Petroleum Exploration and Development, Sinopec Shengli oilfield Company) | Wang, Qianjun (Research Institute of Petroleum Exploration and Development, Sinopec Shengli oilfield Company) | Wang, Jinduo (Research Institute of Petroleum Exploration and Development, Sinopec Shengli oilfield Company)
ABSTRACT The existing AVO inversion method based on Bayesian theory only considers the prior distribution of noise in seismic data, but does not take into account the prior distribution of reflection coefficient. In view of this problem, the paper studies the statistical distribution characteristics of seismic reflection coefficient, and proposes the concept and idea of AVO inversion that considering the statistical distribution of reflection coefficient. Moreover, we construct the AVO inversion objective function containing the statistical distribution that constraints of reflection coefficient under the framework of AVO inversion based on Bayesian theory. Considering the difference in the prior distribution of stratigraphic reflection coefficient in different regions, we choose the generalized extremum distribution(GEV) with parameter adaptive adjustment features to describe the statistical distribution of reflection coefficient. Aiming at the high nonlinearity of GEV, the objective function in this paper is solved by an improved particle swarm optimization (PSO) algorithm. The experimental analysis results not only verify the effectiveness and reliability of the proposed method, but also confirm the importance and criticality of constraints of the reflection coefficient distribution. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 9:20 AM Presentation Time: 10:35 AM Location: Poster Station 7 Presentation Type: Poster
Wang, Z.Y. (College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China) | Bai, W.L. (College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China) | Liu, H. (Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China)
FD scheme is a multi-parameter optimization problem. Consequently, the global optimality cannot be always The finite-difference (FD) scheme is widely used to model guaranteed. Usually we follow the steps below to derive an acoustic and elastic wave propagation. However, the optimized FD scheme based on the optimization methods: numerical dispersion will affect the accuracy and efficiency first we formulate the objective function containing the FD of the modeling due to approximate the differential operator coefficients and set a margin of error, secondly using kinds with the difference operator. In this abstract, a new of optimization methods searching for the optimal solution optimized FD scheme is proposed based on the improved of FD coefficients within a certain range to achieve the particle swarm optimization (PSO) algorithm. We improve optimization of the FD scheme. The key of the optimization the PSO algorithm by introducing the strategies of local of the FD scheme based on the optimization methods is the learning and global learning. Then, the improved PSO selection of optimization methods and margin of error.
ABSTRACT In this work, we illustrate an example in estimating the macromodel of velocities in the subsurface through the use of global optimization methods (GOMs). The optimization problem is solved using DEAP (Distributed Evolutionary Algorithms in Python) and Devito, Python frameworks for evolutionary and automated finite difference computations, respectively. We implement a Particle swarm optimization (PSO) with an "elitism strategy" on top of DEAP, leveraging its transparent, simple and coherent environment for implementing of evolutionary algorithms (EAs). The high computational effort, due to the huge number of cost function evaluations (each one demanding a foward modeling step) required by PSO, is alleviated through the use of Devito. The combined use of both frameworks yields not only an efficient way of providing acoustic macro models of the P-wave velocity field (Vp), but also significantly reduces the amount of geophysicist effort in fulfilling this task. Presentation Date: Monday, September 16, 2019 Session Start Time: 1:50 PM Presentation Start Time: 3:30 PM Location: Poster Station 12 Presentation Type: Poster
Shragge, Jeffrey (Colorado School of Mines) | Yang, Jihyun (Colorado School of Mines) | Issa, Nader A (Terra15 Pty Ltd) | Roelens, Michael (Terra15 Pty Ltd) | Dentith, Michael (University of Western Australia) | Schediwy, Sascha (University of Western Australia)
ABSTRACT Distributed Acoustic Sensing (DAS) is a rapidly growing novel sensing method for seismic data acquisition. DAS arrays are particularly well-suited for dense recording of low-frequency ambient surface waves on long (>5 km) linear sections of deployed optical fiber cable. Applying multi-channel analysis of surface waves (MASW) to ambient wavefield DAS recordings characterized by a large number of sensing points and long recording times may enable 1D characterization of the S-wave velocity profile to depths of 750 m or greater. We present a low-frequency ambient wavefield investigation using a DAS dataset acquired on an array deployed in suburban Perth, Australia. We extract storm-induced swell noise from the nearby Indian Ocean in a low-frequency band (0.1–1.8 Hz) and generate virtual shot gathers by applying cross-correlation and deconvolution seismic interferometric analyses. The resulting gathers are transformed into dispersion images through two different methods: phase shift and high-resolution linear Radon transform. To recover the near-surface S-wave velocity model, we first pick and then invert the recovered 1D Rayleigh-wave dispersion curves using a particle-swarm optimization algorithm. Inversion results show that low-frequency ambient-wavefield DAS data can constrain the Vs model to 750 m depth, which helps validate the potential of DAS technology as a tool for large-scale surface-wave investigation. Presentation Date: Monday, September 16, 2019 Session Start Time: 1:50 PM Presentation Time: 3:30 PM Location: 221C Presentation Type: Oral
Abstract 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.
Results are presented on the application of an optimization solver for full-waveform inversion (FWI) in a synthetic microseismic monitoring scenario. The optimization solver is based on a heuristic algorithm that does not require knowledge about the gradient of the cost function. The optimized variables are the sources origin times, locations and moment tensors, and the earth model velocities, Thomsen parameters and depths of interfaces. The application to noise-free data offers encouraging results to continue the assessment of the algorithm in more realistic scenarios of microseismic monitoring.
Presentation Date: Tuesday, October 16, 2018
Start Time: 8:30:00 AM
Location: 208A (Anaheim Convention Center)
Presentation Type: Oral
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