Hybrid Approach to Reservoir Modeling Based on Modern CPU and GPU Computational Platforms

2017 ◽  
Author(s):  
Alexey Telishev ◽  
Kirill Bogachev ◽  
Vasilii Shelkov ◽  
Dmitry Eydinov ◽  
Hau Tran
Keyword(s):  
Hybrid Approach ◽  
Reservoir Modeling

Hybrid Approach to Reservoir Modeling Based on Modern CPU and GPU Computational Platforms (Russian)

2017 ◽  
Author(s):  
Alexey Telishev ◽  
Kirill Bogachev ◽  
Vasilii Shelkov ◽  
Dmitry Eydinov ◽  
Hau Tran
Keyword(s):  
Hybrid Approach ◽  
Reservoir Modeling

The Implementation of Hybrid Parallel Computation for Complex and Fine Reservoir Model Using Cluster Technology

2021 ◽  
Author(s):  
B. Darmawan
Keyword(s):  
Modeling And Simulation ◽  
Message Passing Interface ◽  
Cluster Computing ◽  
Performance Test ◽  
Hybrid Approach ◽  
Reservoir Modeling ◽  
Computing Technology ◽  
Reservoir Model ◽  
Cluster Technology ◽  
Simulation Time

Pertamina EP plays an important role in maintaining the oil production supply for national energy stability. Thus, they bear a great responsibility to accelerate all the development plans and execute them in timely manner. However, there is big challenge in the realization of those plans since they are not fully equipped with the advance computing technology to boost the reservoir modeling and simulation phase. Therefore, the effort on finalizing and executing of 33 Plan of development (POD) projects within 5 years was looked like a never-ending project. To face the challenge, Pertamina EP evaluated the possibility to create a cluster technology that can accommodate high intensity of simulation numbers and high load of simulation process. The evaluation process covers: compiling, sorting and selecting the analog reservoir model (highest grid number and longest simulation time), benchmarking and performance test to get the most optimum cluster configuration. Supercomputer was then procured and set based on the optimized model, then completed by implementing the test on three most extreme POD models. This paper described the success story and innovation of a complex simulation and finer scale reservoir model using the hybrid parallel-computing technology with a set of 8 nodes high performing computer. Three models were tested with satisfying results. This paper discusses the parallel scalability of complex computing systems of multi-CPU clusters. Multi-CPU distributed memory computing system is proven to be able to improve and accelerate the reservoir modeling and simulation time, when it is used in combination with a new so called “hybrid” approach. In this approach, the common Message Passing Interface (MPI) synchronization between the cluster nodes is being interleaved with a shared memory system thread-based synchronization at the node level. The model with the longest simulation time has been accelerated by magnitude of 60%. The most exhausted model with highest number of simulation steps has been accelerated by magnitude of 80%. The model with the greatest number of grid (21.7 million active grids) has finally finished its simulation just in 27 minutes where previously was impossible to have it open and run. The successful study case is then followed by the implementation of the cluster computing technology for two pilot POD projects which led to the very good result. With this improvement, Pertamina EP can finally perform the probabilistic simulation as recommended by SKKMIGAS in PTK Rev-2/2018. It is now possible to run all 33 structures of multiple reservoir realizations for each POD.

scholarly journals Direct Depth Domain Bayesian AVO Inversion

Geophysics ◽  
2021 ◽  
pp. 1-46
Author(s):  
Madhumita Sengupta ◽  
Houzhu Zhang ◽  
Yang Zhao ◽  
Mike Jervis ◽  
Dario Grana
Keyword(s):  
Reservoir Characterization ◽  
Time Window ◽  
Hybrid Approach ◽  
Spatial Location ◽  
Seismic Inversion ◽  
Reservoir Modeling ◽  
Model Parameters ◽  
New Approach ◽  
Avo Inversion ◽  
Computational Speed

We present a new approach to perform Bayesian linearized amplitude-versus-offset (AVO) inversion directly in the depth domain using non-stationary wavelets. Bayesian linearized AVO inversion, which is a hybrid approach combining physics-based models with statistical learning, has gained immense popularity in the past decade because of its superior computational speed and its ability to estimate uncertainties in the inverted model parameters. Bayesian linearized AVO inversion is typically performed on time-domain seismic data; therefore, depth-imaged seismic cannot be inverted directly using this method, and would require depth-to-time conversion before AVO inversion can be done. Subsequently, time-to-depth conversion of the inverted volumes would be required for reservoir modeling and well-placement. Domain-conversions introduce additional sources of uncertainty in the geophysical workflows. Another drawback of conventional AVO inversion techniques is that the seismic wavelet is assumed to be stationary, and this assumption leads to a restriction in the length of the time-window over which the inversion can be performed. Therefore, AVO inversion is usually restricted to a narrow time window around the target of interest, and in case multiple targets are present at different depths, multiple inversions have to be run on the same seismic volume if we use traditional AVO inversion. AVO inversion in the depth-domain using non-stationary wavelets (or point-spread functions) is a fairly recent development, and has been previously presented in an iterative formulation that is computationally intensive compared to Bayesian linearized AVO inversion. Implementing linearized Bayesian inversion directly in the depth-domain using non-stationary wavelets is a convenient new approach that takes advantage of superior computational speed and uncertainty quantification without compromising the accurate spatial location that depth imaging provides. Bringing these two schools of thought together creates a novel, unique, and powerful seismic inversion technique that can be useful for quantitative interpretation and reservoir characterization.

Surgical and endovascular hybrid approach in peripheral arterial disease of the lower limbs

VASA ◽  
2016 ◽  
Vol 45 (5) ◽  
pp. 417-422 ◽  
Author(s):  
Anouk Grandjean ◽  
Katia Iglesias ◽  
Céline Dubuis ◽  
Sébastien Déglise ◽  
Jean-Marc Corpataux ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Peripheral Arterial Disease ◽  
Limb Salvage ◽  
Hybrid Approach ◽  
Arterial Disease ◽  
Early Mortality ◽  
Secondary Patency ◽  
Limb Salvage Rate ◽  
Peripheral Arterial ◽  
Salvage Rate

Abstract. Background: Multilevel peripheral arterial disease is frequently observed in patients with intermittent claudication or critical limb ischemia. This report evaluates the efficacy of one-stage hybrid revascularization in patients with multilevel arterial peripheral disease. Patients and methods: A retrospective analysis of a prospective database included all consecutive patients treated by a hybrid approach for a multilevel arterial peripheral disease. The primary outcome was the patency rate at 6 months and 1 year. Secondary outcomes were early and midterm complication rate, limb salvage and mortality rate. Statistical analysis, including a Kaplan-Meier estimate and univariate and multivariate Cox regression analyses were carried out with the primary, primary assisted and secondary patency, comparing the impact of various risk factors in pre- and post-operative treatments. Results: 64 patients were included in the study, with a mean follow-up time of 428 days (range: 4 − 1140). The technical success rate was 100 %. The primary, primary assisted and secondary patency rates at 1 year were 39 %, 66 % and 81 %, respectively. The limb-salvage rate was 94 %. The early mortality rate was 3.1 %. Early and midterm complication rates were 15.4 % and 6.4 %, respectively. The early mortality rate was 3.1 %. Conclusions: The hybrid approach is a major alternative in the treatment of peripheral arterial disease in multilevel disease and comorbid patients, with low complication and mortality rates and a high limb-salvage rate.

Delayed Closure of Multiple Muscular Ventricular Septal Defects in an Infant after Coarctation Repair and a Hybrid Procedure�A Case Report

2011 ◽  
Vol 14 (1) ◽  
pp. 67 ◽  
Author(s):  
Ireneusz Haponiuk ◽  
Maciej Chojnicki ◽  
Radosaw Jaworski ◽  
Jacek Juciski ◽  
Mariusz Steffek ◽  
...  
Keyword(s):  
Septal Defect ◽  
Color Doppler ◽  
Hybrid Approach ◽  
Ventricular Septal Defects ◽  
Color Doppler Echocardiography ◽  
Septal Defects ◽  
Coarctation Repair ◽  
Step Procedure ◽  
The Right ◽  
Delayed Closure

There are several strategies of surgical approach for the repair of multiple muscular ventricular septal defects (mVSDs), but none leads to a fully predictable, satisfactory therapeutic outcome in infants. We followed a concept of treating multiple mVSDs consisting of a hybrid approach based on intraoperative perventricular implantation of occluding devices. In this report, we describe a 2-step procedure consisting of a final hybrid approach for multiple mVSDs in the infant following initial coarctation repair with pulmonary artery banding in the newborn. At 7 months, sternotomy and debanding were performed, the right ventricle was punctured under transesophageal echocardiographic guidance, and the 8-mm device was implanted into the septal defect. Color Doppler echocardiography results showed complete closure of all VSDs by 11 months after surgery, probably via a mechanism of a localized inflammatory response reaction, ventricular septum growth, and implant endothelization.

TRANSIENT MODELING OF CPL FOR TERRESTRIAL APPLICATION, PART B: RESERVOIR MODELING IMPROVEMENT

2014 ◽  
Vol 5 (1-4) ◽  
pp. 271-278
Author(s):  
N. Blet ◽  
Vincent Ayel ◽  
Yves Bertin ◽  
Cyril Romestant ◽  
Vincent Platel
Keyword(s):  
Reservoir Modeling ◽  
Transient Modeling

A Hybrid Approach for Virtual Machine Migration in Cloud Computing Environment

2017 ◽  
Vol 7 (9) ◽  
pp. 30 ◽  
Author(s):  
Ramandeep Kaur
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Heuristic Algorithms ◽  
Hybrid Approach ◽  
Cloud Infrastructure ◽  
Virtual Machine Migration ◽  
Bee Colony ◽  
Effective Manner ◽  
Effective Performance

A lot of research has been done in the field of cloud computing in computing domain.  For its effective performance, variety of algorithms has been proposed. The role of virtualization is significant and its performance is dependent on VM Migration and allocation. More of the energy is absorbed in cloud; therefore, the utilization of numerous algorithms is required for saving energy and efficiency enhancement in the proposed work. In the proposed work, green algorithm has been considered with meta heuristic algorithms, ABC (Artificial Bee colony .Every server has to perform different or same functions. A cloud computing infrastructure can be modelled as Primary Machineas a set of physical Servers/host PM1, PM2, PM3… PMn. The resources of cloud infrastructure can be used by the virtualization technology, which allows one to create several VMs on a physical server or host and therefore, lessens the hardware amount and enhances the resource utilization. The computing resource/node in cloud is used through the virtual machine. To address this problem, data centre resources have to be managed in resource -effective manner for driving Green Cloud computing that has been proposed in this work using Virtual machine concept with ABC and Neural Network optimization algorithm. The simulations have been carried out in CLOUDSIM environment and the parameters like SLA violations, Energy consumption and VM migrations along with their comparison with existing techniques will be performed.

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