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Designing a genetic algorithm for efficient calculation in time-lapse gravity inversion
Wahyudi E.J.a, Santoso D.a, Kadir W.G.A.a, Alawiyah S.a
a Applied Geophysics Research Group, Institut Teknologi Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]As an advanced application of soft computation in the oil and gas industry, genetic algorithms (GA) can contribute to geophysical inversion problems in order to achieve better results and efficiency in the computational process. Time-lapse gravity responses to pore-fluid density changes can be modeled to provide the density distribution in the subsurface. This paper discusses the progress of work in inverse modeling of time-lapse gravity data using value encoding with alphabet formulation. The alphabet formulation was designed to provide the solution for positive and negative density change with respect to a reference value (0 gr/cc). The inversion was computed using a genetic algorithm as the optimization method. Working with genetic algorithms, time-intensive computational processes are a challenge, so the algorithm was designed in steps through the evaluation of a GA operator performance test. The performances of several combinations of GA operators (selection, crossover, mutation, and replacement) were tested with a synthetic model of a single-layer reservoir. Sharp boundaries of density changes in the reservoir layer were derived from interpretation of the averaged calculation of several model samples. Analysis showed that the combination of stochastic universal sample-multipoint crossover-quenched simulated annealing per generation-no duplicity achieved the most promising results. © 2014 Published by ITB Journal Publisher.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Genetic algorithm,Inverse modeling,Optimization,Reservoir monitoring,Time-lapse gravity[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.5614/j.eng.technol.sci.2014.46.1.4[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]