Simulated annealing‐based seismic inversion

Pedersen, Jacob; Vestergaard, Peter D.; Zimmerman, Theo

doi:10.1190/1.1889201

Cited by 2 publications

(2 citation statements)

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“…Transferring genetic data between paired models is the basic tenet of a crossover. The crossover could also be seen as a means of information sharing between paired models, leading to the creation of new models 13 . Geophysical difficulties might involve either single-point crossover or multipoint crossover.…”

Section: Methodology Genetic Algorithmmentioning

confidence: 99%

Qualitative and quantitative reservoir characterization using seismic inversion based on particle swarm optimization and genetic algorithm: A comparative case study

Kant

Maurya

Singh

2023

Preprint

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Accurate reservoir characterization is necessary to effectively monitor, manage, and increase production. A seismic inversion methodology using a genetic algorithm (GA) and particle swarm optimization (PSO) technique has been devised in thisstudy to characterize the reservoir both qualitatively and quantitatively. It has always been difficult and expensive to disclose deeper reservoirs in exploratory operations when using traditional approaches for reservoir characterization hence inversion based on advanced technique (based on GA and PSO) is proposed in this study. The main goal is to use GA and PSO to significantly lower the fitness (error) function between real seismic data and modeled synthetic data, which will allow us to estimate subsurface properties and, ultimately, accurately characterize the reservoir. Using two synthetic data and one real data from the Blackfoot field in Canada, the study examined subsurface acoustic impedance and porosity in the inter-well zone. Porosity and acoustic impedance are layer features, but seismic data is an interface property, hence these characteristics provide more useful and applicable reservoir information. The inverted results aid in the understanding of seismic data by providing incredibly high-resolution images of the subsurface. Both the GA and the PSO algorithms deliver outstanding results for both simulated and real data. The inverted section accurately anticipated a high porosity zone (> 20%) that supported thehigh seismic amplitude anomaly by having a low acoustic impedance (6000-8500m/s∗ g/cc). This unusual zone is categorized as a reservoir (sand channel) and is located in the 1040–1065 ms time range. In this inversion process, after 400 iterations, thefitness error falls from 1 to 0.88 using GA optimization, compared to 1 to 0.25 using PSO. The convergence time for GA is 670680 seconds, but the convergence time for PSO optimization is 356400 seconds, showing that the former requires 88% more time than the latter. The study recommends PSO application over GA in any E & P project because PSO inversion delivers faster convergence times, excellent error minimization, and high-resolution subsurface data compared to inversion utilizing GA optimization.

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Section: Methodology Genetic Algorithmmentioning

confidence: 99%

Qualitative and quantitative reservoir characterization using seismic inversion based on particle swarm optimization and genetic algorithm: A comparative case study

Kant

Maurya

Singh

2023

Preprint

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“…Subsequently, control variables are optimized step by step by iterative method. Other derivative-free algorithms such as SID-PSM algorithm [38], NEWUOA algorithm [39,40], genetic algorithm (GA) [41], particle swarm optimization (PSO) [42], and simulated annealing algorithm (SA) [43] also get some attention, but the calculation efficiency still has a lot of room for improvement. To mitigate this problem, Zhao et al later on proposed an updated algorithm named quadratic interpolation model-based approximate gradient algorithm (QIM-AG) [44].…”

Section: Introductionmentioning

confidence: 99%

A Novel Data-Driven Model for Dynamic Prediction and Optimization of Profile Control in Multilayer Reservoirs

et al. 2021

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Establishing reservoir numerical simulation and profile control optimization methods considering the mechanism of profile control has always been a difficult research problem at home and abroad. In this paper, firstly, a physics-based data-driven model was established on daily production data of injection and production wells following the principle of material balance. Key parameters including transmissibility, control pore volume, water injection allocation factors, and injection efficiency are derived directly from history matched model, and the dominated flow channels could be quantitatively identified. Then, combined with the evaluation results of the plugging ability of the plugging agent, imaginary well nodes are added to the existing interwell relationship to characterize the heterogeneity of interwell-specific parameters. This process performs flow processing along the interwell control units, forming a new and rapid method for simulation and prediction. Lastly, based on the calculated interwell transmissibility, water injection efficiency, and allocation factors, injection wells with low water injection efficiency can be preferentially selected as profile control wells. In addition, taking the production rates, injection rates, and the amount of plugging agent as optimization variables, we established an optimal control mathematical model and realized the parameter optimization method of the profile control. We demonstrated the results of one conceptual model and two indoor experiments to verify the feasibility of the proposed method and completed two actual field applications. Model validation and actual field application show that the proposed method successfully eliminates the complicated geological modeling procedure and the tedious calculation process associated with the profile control treatment in traditional numerical simulation methods. The calculation speed improves tens or hundreds of times, and water channeling paths are accurately identified. Most importantly, this method realizes the overall decision-making of profile control well selection, dynamic production prediction, and parameter optimization of profile control measures quickly and accurately by mainly using the daily production data of wells. The findings of this study can help for better understanding of the optimization design and application of on-site profile control schemes in large-scale oilfields.

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Simulated annealing‐based seismic inversion

Cited by 2 publications

References 1 publication

Qualitative and quantitative reservoir characterization using seismic inversion based on particle swarm optimization and genetic algorithm: A comparative case study

Qualitative and quantitative reservoir characterization using seismic inversion based on particle swarm optimization and genetic algorithm: A comparative case study

A Novel Data-Driven Model for Dynamic Prediction and Optimization of Profile Control in Multilayer Reservoirs

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