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ABSTRACT We use the mixed-integer nonlinear optimization algorithm called Particle Swarm Optimization and Mesh Adaptive Direct Search to optimize the design of seismic surveys. Due to the conflicting goals of obtaining a good subsurface illumination at the lowest possible cost, we apply a bi-objective optimization strategy that searches the best options in the illumination and cost senses while builds a Pareto front that shows the trade-off between illumination and cost and allows the survey designer to choose the specific amount of each one of them. The Particle Swarm Optimization part is used to escape local minima and the mixed-integer part is used to deal with integer aspects of a seismic survey design like the number of receivers and sources, to name but a few. Presentation Date: Monday, September 25, 2017 Start Time: 3:55 PM Location: Exhibit Hall C/D Presentation Type: POSTER
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
Probabilistic reservoir-properties estimation for anisotropic shales using statistical rock physics and seismic data
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)
ABSTRACT 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.
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Agents (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Evolutionary Systems (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (0.35)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.35)
ABSTRACT 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 Location: 360D Presentation Type: ORAL
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Agents (0.90)
- Information Technology > Artificial Intelligence > Machine Learning > Evolutionary Systems (0.90)
Particle Swarm Optimization method for 1D and 2D MTEM data inversion
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)
ABSTRACT 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
- Geophysics > Electromagnetic Surveying (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling (0.54)
- Geophysics > Seismic Surveying > Seismic Processing (0.46)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Agents (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Evolutionary Systems (1.00)
Abstract 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 (MacBeth et al., 2016) circumvents the petro-elastic modelling with its associated uncertainties and also the need to choose a seismic full-wave or convolutional modelling solution, which are used in conventional simulator to seismic (sim2seis) modelling. The method is tested on an offshore field case study from the Norwegian Sea. In this study we firstly perform a check on the validity and accuracy of the proxy approach following the methodology of (Falahat et al. 2013) as a guide. The results confirm linear superposition between the pressure and saturation effects controlling the seismic data. Next a quasi-history matching is set up - here simulation model realisations are selected by random assignation of the key parameters to define a walk through solution space. After this, both the sim2seis and proxy modelling approach are compared for each realisation against a known reference case. The results show a mean seismic error of lower than 5%, which indicates the possibility to utilise a fixed proxy to model the 4D seismic. Finally, the full seismic history matching loop is implemented, where the sim2seis and the proxy-driven SHM are launched to find the optimal solution for our field. A particle swarm optimization (PSO) algorithm is applied as the optimisation tool, and only seismic data are used in the objective function. In both cases the algorithm converged after 30 iterations, and the optimal solutions of the two schemes are comparable. It is observed that the full sim2seis and proxy-driven SHMs are only marginally different, implying that solution space is similar in both cases. We also observe that in either case, matching to seismic data only can improve the production match. A unique feature of this study is the application of a seismic modelling proxy in the SHM scheme. Despite its relative simplicity, the approach is found not to bias the optimal solution of the more conventional SHM where the full physics of seismic modelling is applied. Meanwhile, this approach can save over 60% of the total computing time compared with the normal procedure, and this helps significantly to achieve a rapid and effective seismic history matching and better define uncertainty with a larger number of realisations.
- Research Report > New Finding (0.88)
- Research Report > Experimental Study (0.54)
- Geology > Geological Subdiscipline > Geomechanics (0.67)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.46)
- Geophysics > Time-Lapse Surveying > Time-Lapse Seismic Surveying (1.00)
- Geophysics > Seismic Surveying (1.00)
- Europe > Norway > Norwegian Sea > Halten Terrace > PL 128 > Block 6608/10 > Norne Field > Tofte Formation (0.99)
- Europe > Norway > Norwegian Sea > Halten Terrace > PL 128 > Block 6608/10 > Norne Field > Not Formation (0.99)
- Europe > Norway > Norwegian Sea > Halten Terrace > PL 128 > Block 6608/10 > Norne Field > Ile Formation (0.99)
- (4 more...)
- Reservoir Description and Dynamics > Reservoir Simulation > History matching (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Four-dimensional and four-component seismic (1.00)
Integrated interpretation of 2D ground-penetrating radar, P-, and S-wave velocity models in terms of petrophysical properties: Assessing uncertainties related to data inversion and petrophysical relations
Tronicke, Jens (University of Potsdam) | Paasche, Hendrik (UFZ โ Helmholtz Center for Environmental Research)
Abstract Near-surface geophysical techniques are extensively used in a variety of engineering, environmental, geologic, and hydrologic applications. While many of these applications ask for detailed, quantitative models of selected material properties, geophysical data are increasingly used to estimate such target properties. Typically, this estimation procedure relies on a two-step workflow including (1)ย the inversion of geophysical data and (2)ย the petrophysical translation of the inverted parameter models into the target properties. Standard deterministic implementations of such a quantitative interpretation result in a single best-estimate model, often without considering and propagating the uncertainties related to the two steps. We address this problem by using a rather novel, particle-swarm-based global joint strategy for data inversion and by implementing Monte Carlo procedures for petrophysical property estimation. We apply our proposed workflow to crosshole ground-penetrating radar, P-, and S-wave data sets collected at a well-constrained test site for a detailed geotechnical characterization of unconsolidated sands. For joint traveltime inversion, the chosen global approach results in ensembles of acceptable velocity models, which are analyzed to appraise inversion-related uncertainties. Subsequently, the entire ensembles of inverted velocity models are considered to estimate selected petrophysical properties including porosity, bulk density, and elastic moduli via well-established petrophysical relations implemented in a Monte Carlo framework. Our results illustrate the potential benefit of such an advanced interpretation strategy; i.e., the proposed workflow allows to study how uncertainties propagate into the finally estimated property models, while concurrently we are able to study the impact of uncertainties in the used petrophysical relations (e.g., the influence of uncertain, user-specified parameters). We conclude that such statistical approaches for the quantitative interpretation of geophysical data can be easily extended and adapted to other applications and geophysical methods and might be an important step toward increasing the popularity and acceptance of geophysical tools in engineering practice.
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (1.00)
- Geophysics > Electromagnetic Surveying (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Evolutionary Systems (0.89)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Agents (0.67)
ABSTRACT The elastic parameters ( and density) of a multilayered earth can be derived from amplitude versus offset (AVO) data. The problem is non-unique nature, as different combinations of elastic parameters may yield the same AVO response. An attempt has been made to estimate the elastic parameters using a constrained AVO inversion in which the derived from the travel time data (unique in nature) is kept constant during the AVO inversion. Methods based on local linearization fail if the starting model is too far from the true model. FDR PSO, being very robust and proficient in dealing with highly non-linear problems, is used for both the travel time and AVO inversions yielding an improved methodology. The inversion scheme, applied on synthetic data (both noise free and 2% noise added) shows very promising results. Presentation Date: Tuesday, October 18, 2016 Start Time: 8:25:00 AM Location: Lobby D/C Presentation Type: POSTER
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic modeling (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Agents (0.73)
- Information Technology > Artificial Intelligence > Machine Learning > Evolutionary Systems (0.73)
A hybrid PSOGSA-based inversion of noise corrupted seismic data using singular spectrum-based time slice denoising
Priyadarshi, Shubham Kumar (Indian School of Mines, Dhanbad) | Maiti, Saumen (Indian School of Mines, Dhanbad) | Rekapalli, Rajesh (CSIR-National Geophysical Research Institute, Hyderabad) | Tiwari, Ram Krishna (CSIR-National Geophysical Research Institute, Hyderabad)
ABSTRACT The quality of the surface seismic data plays a very important role in successful reservoir identification. In reservoir geophysics, impedance inversion from noise-corrupted seismic data has always been a challenge. In this paper, we present the application of Time Slice Singular Spectrum Analysis (TSSSA) based de-noising as a pre-filtering for seismic inversion using a hybrid of Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA). We tested the performance of a hybrid PSOGSA based inversion on pure synthetic data, synthetic data with 30% added and its TSSSA de-noised output. The comparison of the inverted seismic data from these data sets was performed. We observed that the impedance computed from the de-noised output shows a good match with the true impedance, whereas the estimated impedance from noisy data deviated from the original value. The TSSSA algorithm provides a robust approach for seismic data de-noising and facilitates accurate impedance estimation using PSOGSA based inversion from noise corrupted seismic data. Presentation Date: Tuesday, October 18, 2016 Start Time: 3:20:00 PM Location: Lobby D/C Presentation Type: POSTER
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Evolutionary Systems (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Agents (0.90)
Abstract Fault interpretation in seismic data is a critical task that must be completed to thoroughly understand the structural history of the subsurface. The development of similarity-based attributes has allowed geoscientists to effectively filter a seismic data set to highlight discontinuities that are often associated with fault systems. Furthermore, there are numerous workflows that provide, to varying degrees, the ability to enhance this seismic attribute family. We have developed a new method, spectral similarity, to improve the similarity enhancement by integrating spectral decomposition, swarm intelligence, magnitude filtering, and orientated smoothing. In addition, the spectral similarity method has the ability to take any seismic attribute (e.g., similarity, curvature, total energy, coherent energy gradient, reflector rotation, etc.), combine it with the benefits of spectral decomposition, and create an accurate enhancement to similarity attributes. The final result is an increase in the quality of the similarity enhancement over previously used methods, and it can be computed entirely in commercial software packages. Specifically, the spectral similarity method provides a more realistic fault dip, reduction of noise, and removal of the discontinuous โstair-stepโ pattern common to similarity volumes.
- Geology > Structural Geology > Fault (1.00)
- Geology > Geological Subdiscipline (0.94)
- Geophysics > Seismic Surveying > Seismic Interpretation (1.00)
- Geophysics > Seismic Surveying > Seismic Processing (0.89)
- Information Technology > Artificial Intelligence > Machine Learning > Evolutionary Systems (0.91)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Agents (0.68)
Pre-Stack Seismic Stochastic Inversion with VFSA-Based PSO Algorithm
Wang, Xiaodan (China University of Petroleum, East China) | Yin, Xingyao (China University of Petroleum, East China) | Wang, Baoli (China University of Petroleum, East China) | Yang, Fusen (China University of Petroleum, East China)
Summary It is well proven that stochastic inversion often results in better vertical resolution than deterministic inversion by making better use of well information, and pre-stack seismic inversion offers the better opportunity of characterizing reservoirs than post-stack inversion in a quantitative fashion. In this paper, we develop a method, pre-stack stochastic inversion, by combining the two advantageous inversions together which is formulated based on the Bayesian framework. In implementation of the method, FFT-MA and GDM algorithms are used to acquire the priori information, and Particle Swarm Optimization (PSO) approach based on the Very Fast simulated annealing algorithm (VFSA-based PSO) is chosen to obtain posterior distribution of P-wave velocity, S-wave velocity, density, and other elastic parameters. We demonstrate through model and real data analysis that the proposed method works very fast and provides high accuracy inversion results. Introduction One of the key aims of geophysical exploration is to infer the elastic parameters of the subsurface based on bandseismic and logging data. However, it has proven to be challenging due to that fact that seismic data are bandlimited, and logging data lack the regional coverage although they have high vertical resolution. Therefore, in real practice, how to take advantage of all useful information of seismic and log data becomes the key theme of seismic inversion. In this regard, stochastic inversion is advantageous over deterministic inversion like seismic amplitude versus offset (AVO) and conventional Sparse Spike inversion, although the latter play an important role in reservoir characterization. In this study, we combine stochastic inversion with prestack inversion and apply a Bayesian formulation of the solution to the seismic inverse problem. It is readily apparent that the proposed pre-stack stochastic inversion has higher resolution in comparison with conventional deterministic inversion. To date, Bortoli et al. firstly proposed geo-statistical inversion in 1993. After that, Haas and Dubrule (1994) developed the stochastic inversion method based on single trace simulation, but the computational efficiency is low. Debeye (1996), Sams (1999) and Contreras (2005) used point perturbation to replace the single trace repeated simulation, and expanded the stochastic inversion algorithm. However, the problem of time-consuming and memory-consuming still exist. In our study we use the Fast Fourier Transform - Moving Average (FFT-MA) (Le Ravalec, 2000) algorithm to enhance computational efficiency and reduce the memory requirements. Meanwhile, we introduce the Gradual Deformation Method (GDM) (Hu, 1998) to ensure fast convergence of the inversion solutions.
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic modeling (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Agents (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Evolutionary Systems (1.00)