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Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Qualitative and quantitative reservoir characterization using seismic inversion based on particle swarm optimization

Ravi Kant1, S P Maurya2, K H Singh3

  • 1Department of Geophysics, Banaras Hindu University, Varanasi, 221005, India.

Scientific Reports
|September 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces genetic algorithm (GA) and particle swarm optimization (PSO) for seismic inversion, improving reservoir characterization. PSO demonstrated faster convergence and lower error than GA for estimating subsurface properties like porosity.

Keywords:
Acoustic impedanceGenetic algorithmGlobal optimizationParticle swarm optimizationPorosity

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Area of Science:

  • Geophysics and Reservoir Engineering
  • Computational Intelligence in Earth Sciences

Background:

  • Accurate reservoir characterization is crucial for effective oil and gas production management.
  • Conventional methods for mapping deep reservoirs are often costly and challenging.
  • Advanced seismic inversion techniques offer a promising alternative for detailed subsurface analysis.

Purpose of the Study:

  • To develop and compare a seismic inversion methodology using genetic algorithm (GA) and particle swarm optimization (PSO).
  • To quantitatively and qualitatively characterize reservoirs by estimating subsurface properties.
  • To reduce the fitness (error) function between real seismic data and synthetic modeled data.

Main Methods:

  • Seismic inversion utilizing genetic algorithm (GA) and particle swarm optimization (PSO).
  • Estimation of subsurface acoustic impedance and porosity in the inter-well zone.
  • Validation using two synthetic datasets and one real dataset from the Blackfoot field, Canada.

Main Results:

  • Both GA and PSO effectively estimated subsurface properties, yielding high-resolution subsurface images.
  • The inversion accurately delineated a high porosity reservoir zone characterized by low acoustic impedance.
  • PSO achieved a lower final fitness error (0.25 vs. 0.88) and faster convergence (356,400s vs. 670,680s) compared to GA.

Conclusions:

  • The proposed seismic inversion methodology using GA and PSO significantly enhances reservoir characterization.
  • Particle swarm optimization (PSO) is more efficient than genetic algorithm (GA) in terms of convergence speed and accuracy for this application.
  • The technique provides valuable insights into reservoir properties, aiding in exploration and production strategies.