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Energy
Viscoacoustic wave simulation with the lattice Boltzmann method
Xia, Muming (Chinese Academy of Sciences, Chinese Academy of Sciences) | Zhou, Hui (China University of Petroleum (Beijing)) | Jiang, Chuntao (China University of Petroleum (Beijing)) | Tang, Jinxuan (China University of Petroleum (Beijing)) | Wang, Canyun (Chinese Academy of Sciences, Chinese Academy of Sciences) | Yang, Changchun (Chinese Academy of Sciences, Chinese Academy of Sciences)
ABSTRACT The lattice Boltzmann method (LBM), widely used in computational fluid mechanics, is introduced as a novel mesoscopic numerical scheme for viscoacoustic wavefield simulation. Through mathematical derivation, a mapping model between the relaxation time of LBM and the quality factor based on the Kelvin-Voigt model is established, which provides a theoretical background for the comparison of the viscoacoustic wavefields obtained, respectively, by LBM and finite-difference method (FDM) formulated on the traditional wave equation. By defining the transmission and reflection coefficients and adopting a Newton interpolation algorithm to modify the streaming process of the LBM, we have extended the conventional LBM to simulate the wavefields in complex media with acceptable accuracy. A 2D homogeneous model, two 2D layered models, and the modified Marmousi model are tested in the numerical simulation experiments. The simulation results of LBM are comparable to those of FDM, and the relative errors are all within a reasonable range, which can verify the effectiveness of the forward modeling kernel. The modified LBM offers a new numerical scheme in seismology to simulate viscoacoustic wave propagation in complex media and even in porous media considering its flexible boundary condition and high discrete characteristic.
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
Research on Hull Form Optimization of KCS Ship Based on NM Theory
Feng, Baiwei (Key Laboratory of High Performance Ship Technology, Wuhan University of Technology, Ministry of Education, Wuhan, Hubei / School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology Wuhan, Hubei) | Zhou, Hui (Key Laboratory of High Performance Ship Technology, Wuhan University of Technology, Ministry of Education, Wuhan, Hubei / School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology Wuhan, Hubei) | Ma, Chao (Key Laboratory of High Performance Ship Technology, Wuhan University of Technology, Ministry of Education, Wuhan, Hubei / School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology Wuhan, Hubei)
ABSTRACT To meet the requirements for green ships in the IMO ship Energy Efficiency Design Index, based on the self-developed ship form optimization platform (SHIPMDO-WUT), the KCS ship is regarded as the initial hull to reduce its wave-making resistance at a certain speed. The hull form modification is then implemented by the radial basis function (RBF) interpolation technology and the resistance evaluation is carried out by the NM (Neumann-Michell) potential flow theory solver to minimize the wave-making resistance combining with the NSGA-II algorithm. Finally, after completing the optimization of ship resistance performance at the speed of Fr=0.3, the optimized ship is selected for analysis, and the resistance reduction effect of the optimized ship is analyzed through STARCCM+ software. The research results show that: (1) The hull form modification module based on radial basis interpolation technology can produce a smooth hull profile; (2) For the KCS ship, under the premise of meeting the engineering constraints, the automatic optimization method of the hull surface based on numerical simulation can obtain a new ship form with better performance of resistance. INTRODUCTION Ship hull form optimization is one of the effective methods to achieve energy saving and emission reduction. With the development of computer technology and Computational Fluid Dynamics (CFD), its evaluation capability has been enhanced, simulation-based design (SBD) has been listed as a hot issue in the research of numerical simulation technology by the International Tugboat Conference (ITTC), and many scholars at home and abroad have conducted extensive research on ship hull form optimization. Kim (2009) modified Wigley hull profile based on parametric hull representations and NURBS surfaces and Peri et al. (2001) utilized Bรฉ zier Patch to complete the modification of hull geometry. Based on the Rankine source method, Zhang and Percival et al. (2012) obtained an optimized ship hull form with minimum wave-making resistance. Wan Decheng et al. (2020) carried out local deformation of bow, waterline and aft of luxury cruise ship by the FFD (Free-Form Deformation) method, combining with the Neumann-Michell (NM) potential-flow-based solver NMShip-SJTU to complete the optimization of wave-making resistance at the two specified speeds. Shen Tong et al. (2013; 2015) used the radial basis function interpolation method and combined with the hydrodynamic solver Shipflow to optimize the wave-making resistance of KCS and S60 ships, and obtained the ship type with better resistance performance, but the displacement of the optimized ships was reduced, which reduced the ship operation economy to some extent. Hu Chunping et al. (2012) completed the full parametric modeling of KCS by using parametric modeling software CAESES, and combined with hydrodynamic calculation software to complete the study of automatic optimization of KCS profile. Zhan et al. (2012) used a fusion deformation method to generate a series of bulbous bows, realized the automatic optimization of the bulbous bow profile, and got the ship type with less wave-making resistance.
- Research Report > New Finding (0.34)
- Research Report > Experimental Study (0.34)
- Transportation > Marine (1.00)
- Energy > Oil & Gas > Upstream (0.47)
Poststack seismic inversion using a patch-based Gaussian mixture model
Wang, Lingqian (China University of Petroleum) | Zhou, Hui (China University of Petroleum) | Dai, Hengchang (British Geological Survey) | Yu, Bo (China University of Petroleum) | Liu, Wenling (Research Institute of Petroleum Exploration and Development) | Wang, Ning (Northeast Petroleum University)
ABSTRACT Seismic inversion is a severely ill-posed problem because of noise in the observed record, band-limited seismic wavelets, and the discretization of a continuous medium. Regularization techniques can impose certain characteristics on inversion results based on prior information to obtain a stable and unique solution. However, it is difficult to find an appropriate regularization to describe the actual subsurface geology. We have developed a new acoustic impedance inversion method via a patch-based Gaussian mixture model (GMM), which is designed using available well logs. In this method, first, the nonlocal means method estimates acoustic impedance around wells in terms of the similarity of local seismic records. The extrapolated multichannel impedance is then decomposed into impedance patches. Using patched data rather than a window or single trace for training samples to obtain the GMM parameters, which contain local lateral structural information, can provide more impedance structure details and enhance the stability of the inversion result. Next, the expectation maximization algorithm is used to obtain the GMM parameters from the patched data. Finally, we apply the alternating direction method of multipliers to solve the conventional Bayesian inference illustrating the role of regularization and construct the objective function using the GMM parameters. Therefore, the inversion results are compliant with the local structural features extracted from the borehole data. The synthetic and field data tests validate the performance of our method. Compared with other conventional inversion methods, our method shows promise in providing a more accurate and stable inversion result.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic modeling (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.93)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (0.88)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Undirected Networks > Markov Models (0.46)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.46)
ABSTRACT Seismic acoustic impedance inversion plays an important role in subsurface quantitative interpretation. Due to the band-limited property of the seismic record and the discretization of the continuous elastic parameters with a limited sampling interval, the inverse problem suffers from serious ill-posedness. Various regularization methods are introduced into the seismic inversion to make the inversion results comply with the prespecified characteristics. However, conventional seismic inversion methods can only reflect fixed distribution characteristics and do not take into account discretization challenges. We have adopted a new poststack seismic impedance inversion method with upsampling and adaptive regularization. The adaptive regularization is constructed with two trained dictionaries from the true model and upsampled model-based inversion result to capture the features of high- and low-resolution details, and a sparsity-based statistical model is proposed to build the relationship between their sparse representations. The high-resolution components can be recovered based on the prediction model and low-resolution sparse representations, and the parameters of the statistical prediction model can be obtained effectively with conventional optimization algorithms. The synthetic and field data tests show that the model-based inversion is dependent on the sample interval, and our method can reveal more thin layers and enhance the extension of the strata compared with conventional inversion methods. Moreover, the inverted impedance variance of our method matches well with the borehole observations. The tests demonstrate that the interpolated model-based inversion result combined with the sparsity-based prediction model can effectively improve the resolution and accuracy of the inversion results.
- Information Technology > Artificial Intelligence > Representation & Reasoning (0.87)
- Information Technology > Modeling & Simulation (0.75)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models (0.67)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.67)
Impact of Completion Design on Various Infill Scenarios: A Data Driven Permian Case Study
Darneal, Chad (ConocoPhillips Company) | Friehauf, Kyle (ConocoPhillips Company) | McLin, Kristie (ConocoPhillips Company) | Rajappa, Bharath (ConocoPhillips Company) | Zhou, Hui (ConocoPhillips Company) | Hoang, Phuong (ConocoPhillips Company) | Hammond, Justin (ConocoPhillips Company) | Swan, Herbert (ConocoPhillips Company)
Abstract An ongoing challenge in unconventional reservoirs is the significant production degradation (loss of production) realized from child wells drilled adjacent to depleted parent wells. One strategy hypothesized to reduce the realized degradation is to modify the completion design in the child well. The main objective of this case study will be to test this hypothesis and quantify the impact completion design has on child well degradation; specifically, the case focuses on the stage architecture component of completion design defined as the combination of cluster spacing, number of clusters per stage, and stage length. This paper covers an integrated, multi-disciplined review of a unique development situation in the Permian where three different depletion scenarios surround a single well at various well spacings. This data rich review will characterize the SRV (Stimulated Rock Volume) and DRV (Drained Rock Volume) from each of four completion designs within the different depletion scenarios. Data sets include fiberoptic DAS/DTS (Distributed Acoustic/Temperature Sensing) and microseismic during stimulation, along with downhole pressure gauges, chemical tracers, downhole camera for perforation erosion, additional fiber-optic DAS/DTS production logs, and interference (well communication) tests. A single well with four different completion designs surrounded by three different depletion scenarios creates a rare opportunity to analyze the impact completion design has on child well degradation. Eight different forms of data acquisition technologies were used to increase understanding of completion variable impacts to SRV and DRV as well as validate several new cost-effective data acquisition technologies that were successfully trialed for this pilot. The SRV-related data shows fracture interference with offset depletion, but the amount of interference did not conclusively change among the various completion designs tested. Similarly, DRV-related data shows child well degradation when exposed to parent well depletion, but the amount of degradation did not conclusively change among the various completion designs tested. This suggests that factors other than stage architecture are the dominant drivers of well performance. Detailed analysis from the cross-functional team provides multiple perspectives on the results acquired as they pertain to the overall motivating objectives of the pilot.
- North America > United States > Texas > Permian Basin > Delaware Basin (0.99)
- North America > United States > Texas > Fort Worth Basin > Barnett Shale Formation (0.99)
- North America > United States > New Mexico > Permian Basin > Delaware Basin (0.99)
- Information Technology > Communications > Networks (0.50)
- Information Technology > Artificial Intelligence (0.34)
Mechanical Behavior of Bolted Rock Joints Under Constant Normal Stiffness Shear Loading Condition
Cui, Guojian (University of Chinese Academy of Sciences) | Zhang, Chuanqing (University of Chinese Academy of Sciences) | Zhou, Hui (University of Chinese Academy of Sciences) | Lu, Jingjing (University of Chinese Academy of Sciences) | Zeng, Zhiquan (PowerChina Huadong Engineering Corporation Limited) | Cheng, Guangtan (Shandong Agricultural University)
Abstract The mechanical behaviors of rock joints with and without rock bolt are essential for ensuring the safety of rock engineering and determining support scheme in jointed rock masses. This paper experimentally investigated the shear behavior of unbolted and bolted artificial rough joints using direct shear tests under different boundary conditions, where the effects of the initial normal stress, normal stiffness, and joint surface roughness were studied. Both strain-softening and strain-hardening characteristics were observed for shear stress curves, depended on normal boundary conditions and bolting state. Generally, bolted joint showed better shear resistance capacity than unbolted one regardless of boundary conditions, and conventional shear tests would underestimate the shear resistance capacity of rock joints when shear dilatancy was restricted by surrounding rock masses. Bolt contribution in jointed rock masses tended to decrease with the increase of normal stiffness or initial normal stress and increase with the increasing joint surface roughness. Moreover, the failure characteristics of the joint surface after shear tests were identified. Introduction Shear failure of rock joints is frequently encountered in geotechnical engineering, which might induce some severe disasters, such as fault slip rockburst and landslide, and therefore shear behavior of rock joints has a remarkable influence on the stability and safety of rock engineering projects. Rock bolting, an effective reinforcement technique, was broadly used in the fractured rock mass for enhancing the shear resistance of rock joints. Understanding the mechanical behavior of rock joints with and without rock bolt subjected to shearing is essential to determine the reinforcement effect and optimize corresponding support design schemes in practical engineering. Thereby, it's of considerable significance to study and compare the shear behaviors of rock joints with and without rock bolts. Shear behaviors of rock joints are generally evaluated using direct shear tests in the laboratory. A large number of experimental studies have been carried out on both bolted and unbolted rock joints to reveal the parametrical influence on the reinforcement effect and shear behavior, including rock mass conditions, rock joint surface morphology, bolt diameter and type, loading conditions, etc [1-14]. Spang and Egger experimentally investigated the influence of bolt diameter, bolt inclination angle with respect to the shear direction of the joint plane, and normal stress [1]. Li et al. compared the shear behavior of fiberglass (FG) bolt, rock bolt (steel rebar bolt), and cable bolt for the bolt contribution to bolted concrete surface's shear strength, and bolt failure mode [2]. Wang et al. evaluated the acoustic emission counts and characteristics of bolted rock-like joint specimens with different roughness and bolt elongation rates [7]. Wu et al. conducted a series of direct shear tests on six standard roughness profile joints and two natural joints and found that shear strength increased as the increase of joint roughness and established a dimensionless mathematical model to predict the shear behavior of bolt jointed with different joint roughness conditions [9]. Chen et al. concluded that rock bolts could increase the shear strength and shear stiffness of joint specimens [10]. Jalalifar and Aziz demonstrated that the strength of the concrete, bolt rib profile configuration, and bolt pretension load affected the shear resistance, shear displacement and failure mechanism of the reinforced medium when subjected to double shear tests [11]. Chen developed a new method to apply the pull and shear loads to the rock bolt at the same time [12].
ABSTRACT Reasonable low-wavenumber initial models are essential for reducing the nonuniqueness of seismic inversion. A traditional approach to estimate the low-wavenumber models of elastic parameters is well-log interpolation. However, complex geologic structures decrease the accuracy of this method. To overcome these challenges in building prior models, we have developed an interpolation method based on pattern-feature correlation (PFC) inspired by multiple-point geostatistics (MPG). In our interpolation method, we scan a stacked seismic profile using a predefined data template to obtain a geologic pattern around each node in the seismic profile. Each pattern is then converted into several filter scores with the filters defined in the MPG algorithm of the filter-based simulation. We calculate the correlation coefficients of the filter scores among different patterns for the various nodes and define them as PFCs. We construct the initial models from well-log data based on the weighted interpolation method, in which the weighting factors are precisely determined by the PFCs. We build the initial models using our method for synthetic and field data to demonstrate its effectiveness. To verify the validity of the initial models, we apply them to Bayesian linearized inversion. The accuracy of the interpolation and inversion results verifies the excellent performance of our interpolation method. Our method provides a novel and convenient approach that combines seismic and well-log data, which contributes to seismic exploration and geologic modeling.
- Asia > China (0.46)
- North America (0.28)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (0.93)
Data-driven multichannel poststack seismic impedance inversion via patch-ordering regularization
Wang, Lingqian (China University of Petroleum) | Zhou, Hui (China University of Petroleum) | Liu, Wenling (CNPC) | Yu, Bo (China University of Petroleum) | He, Huili (China University of Petroleum) | Chen, Hanming (China University of Petroleum) | Wang, Ning (Northeast Petroleum University)
ABSTRACT Seismic acoustic impedance inversion plays an important role in reservoir prediction. However, single-trace inversion methods often suffer from spatial discontinuities and instability due to poor-quality seismic records with spatially variable signal-to-noise ratios or missing traces. The specified hyperparameters for seismic inversion cannot be suitable to all seismic traces and subsurface structures. In addition, conventional multichannel inversion imposes lateral continuity with a prespecified mathematical model. However, the inversion results constrained with specified lateral regularization are inferior when the subsurface situations violate the hypothesis. A data-driven multichannel acoustic impedance inversion method with patch-ordering regularization is introduced, in which the spatial correlation of seismic reflection is used. The method decomposes the seismic profile into patches and constructs the patch-ordering matrix based on the similarity among seismic patches to record the impedance structural extension. So the patch-ordering matrix can record the spatial extension of the acoustic impedance. Then, a simple regularization with difference operators of varying weights can reduce the random noise presented in the inverted impedance profile, stabilize the inversion result, and enhance the spatial continuity of the layer extension. The objective function for multichannel poststack seismic impedance inversion can be constructed by integrating the observed seismic record and the spatial continuity in the form of patch-ordering regularization, and it can be solved effectively with the limited-memory BFGS algorithm. The synthetic and field data tests illustrate the improvement of accuracy and lateral continuity of inverted results with our method, compared to conventional model-based inversion results.
- Asia > China (0.68)
- North America > United States > Kentucky > Butler County (0.34)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
Data-driven low-frequency signal recovery using deep-learning predictions in full-waveform inversion
Fang, Jinwei (China University of Petroleum-Beijing, National University of Singapore) | Zhou, Hui (China University of Petroleum-Beijing) | Elita Li, Yunyue (National University of Singapore) | Zhang, Qingchen (Chinese Academy of Sciences) | Wang, Lingqian (China University of Petroleum-Beijing) | Sun, Pengyuan (BGP Research and Development Center of CNPC) | Zhang, Jianlei (BGP Research and Development Center of CNPC)
ABSTRACT The lack of low-frequency signals in seismic data makes the full-waveform inversion (FWI) procedure easily fall into local minima leading to unreliable results. To reconstruct the missing low-frequency signals more accurately and effectively, we have developed a data-driven low-frequency recovery method based on deep learning from high-frequency signals. In our method, we develop the idea of using a basic data patch of seismic data to build a local data-driven mapping in low-frequency recovery. Energy balancing and data patches are used to prepare high- and low-frequency data for training a convolutional neural network (CNN) to establish the relationship between the high- and low-frequency data pairs. The trained CNN then can be used to predict low-frequency data from high-frequency data. Our CNN was trained on the Marmousi model and tested on the overthrust model, as well as field data. The synthetic experimental results reveal that the predicted low-frequency data match the true low-frequency data very well in the time and frequency domains, and the field results show the successfully extended low-frequency spectra. Furthermore, two FWI tests using the predicted data demonstrate that our approach can reliably recover the low-frequency data.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (0.62)
An accelerating strategy in stochastic inversion
Yu, Bo (State Key Laboratory of Petroleum Resources and Prospecting, CNPC Key Lab of Geophysical Exploration, and China University of Petroleum) | Zhou, Hui (State Key Laboratory of Petroleum Resources and Prospecting, CNPC Key Lab of Geophysical Exploration, and China University of Petroleum) | Wang, Lingqian (State Key Laboratory of Petroleum Resources and Prospecting, CNPC Key Lab of Geophysical Exploration, and China University of Petroleum) | Chen, Hanming (State Key Laboratory of Petroleum Resources and Prospecting, CNPC Key Lab of Geophysical Exploration, and China University of Petroleum) | Huang, Weilin (State Key Laboratory of Petroleum Resources and Prospecting, CNPC Key Lab of Geophysical Exploration, and China University of Petroleum) | Shang, Guojun (State Key Laboratory of Petroleum Resources and Prospecting, CNPC Key Lab of Geophysical Exploration, and China University of Petroleum)
Stochastic seismic inversion can integrate diverse datasets to estimate the spatial distribution of subsurface elastic properties. High-resolution stochastic inversion results are significant in the development stage of an oil field. Nevertheless, existing statistical inversion approaches are commonly restricted by the heavy calculation burden. To address this issue, we propose a strategy to accelerate stochastic inversion. Based on a Bayesian linearized inversion theory, we propose a feasible and efficient stochastic inversion. Using the proposed method, we can not only obtain as good stochastic inversion results as the conventional stochastic inversion methods, but also avoid the heavy calculation burdens of forward simulation and computation the inverse of a complex kernel matrix. We test this method by a section of field data, and compare it with the conventional stochastic inversion method. The test result illustrates the effectiveness of this method. Presentation Date: Monday, October 12, 2020 Session Start Time: 1:50 PM Presentation Time: 2:15 PM Location: Poster Station 9 Presentation Type: Poster