Four-Dimension Seismic Analysis in Carbonate: A Closed Loop Study

• Published in: Applied sciences 2022-10, Vol.12 (19), p.9438
• Main Authors: Mahgoub, Mohamed, Bashir, Yasir, Bery, Andy Anderson, Noufal, Abdelwahab
• Format: Article
• Language: English
• Subjects: carbonate reservoir; co-processing; heterogeneous; machine learning
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app12199438

Four-dimensional seismic analysis is an effective reservoir surveillance tool to track the changes of fluid and pressure phases in the oil and gas reservoirs over time of the baseline and monitoring seismic acquisition. In practice, the 4D seismic signal associated with such changes may be negligible, especially in heterogeneous carbonate reservoirs. Therefore, 4D seismic analysis is a model for integrating various disciplines in the oil and gas industry, such as seismic, petrophysics, reservoir engineering, and production engineering. In this study, we started the 4D seismic workflow with a 1D well-based 4D feasibility study to detect the likelihood of 4D signals before performing 4D seismic co-processing of the baseline and monitoring surveys starting from the seismic field data of both datasets. As part of a full 4D seismic co-processing of the baseline and monitor surveys, 4D seismic metric attributes were analyzed over the survey area to measure the improvement in seismic acquisition repeatability for the scarce 1994 baseline seismic and the 2014 monitor seismic survey. For the monitor survey, a 4D time-trace shift was performed using the baseline survey as a reference to measure the time shifts between the baseline and monitor surveys at 20-year intervals. The 4DFour-dimensional dynamic trace warping was followed by a 4D seismic inversion to compare the 4D difference in the seismic inverted data with the difference in seismic amplitude. The seismic inversion helped overcome noise, multiple contaminations, and differences in dynamic amplitude range between the baseline and monitor seismic surveys. We then examined the relationship between well logs and seismic volumes by predicting a volume of log properties at the well locations of the seismic volume. In this method, we computed a possibly nonlinear operator that can predict well logs based on the properties of adjacent seismic data. We then tested a Deep Feed Forward Neural Network (DFNN) on six wells to adequately train and validate the machine learning approach using the baseline and monitoring seismic inverted data. The objective of trying such a deep machine learning approach was to predict the density and porosity of both the baseline and the monitoring seismic data to validate the accuracy of the 4D seismic inversion and evaluate the changes in reservoir properties over a time-lapse of 20 years of production from 1994 to 2014. Finally, we matched the 4D seismic signal with changes in reservoir