4D seismic co-processing pre-stack depth migration for scarce acquisition repeatability

Mahgoub, Mohamed; Bashir, Yasir; Bery, Andy Anderson; Noufal, Abdel Wahab

doi:10.1007/s13202-022-01541-x

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…To compensate for the poor repeatability of the acquisition geometry between the baseline and monitor surveys (different coordinates of the source and receiver, different azimuths, and the number of obstructions), 4D seismic co-processing with field tapes for both the baseline and monitor surveys was used in this study. Deliberate full seismic 4D co-processing starting from the seismic field records is more accurate and robust, and thus far more repeatable, than partial seismic 4D co-processing with Kirchhoff Pre-stack depth migration (KPSDM) or Least Square Pre-stack depth migration (LSM) [12].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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 production properties, investigating the mechanism underlying the observed 4D signal. It was found that the detectability of 4D signals is primarily related to changes in fluid saturation and pressure changes in the reservoir, which increased from 1994 to 2014. This innovative closed-loop research proved that the low repeatability of seismic acquisition can be compensated by optimal 4D seismic co-processing with a complete integration workflow to assess the reliability of the 4D seismic observed signal.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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Cohesive approach for determining porosity and P-impedance in carbonate rocks using seismic attributes and inversion analysis

Bashir,

Siddiqui,

Morib

et al. 2024

J Petrol Explor Prod Technol

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The assessment of hydrocarbon flow through seismic and well-log data presents a persistent challenge in determining porosity. The acoustic impedance section provides a visual representation of the layers, while the raw seismic data showcase the subsurface reflectors that exist within the rock layers. The accuracy of acoustic impedance is widely acknowledged to surpass that of seismic data as a representation of reality. The primary objective of this study is to convert seismic reflector data into acoustic impedance values, which provide insights into the layer properties based on lithology. This approach enhances the accuracy of seismic inversion results by aligning them more closely with actual geological conditions. Seismic inversion is employed to ascertain the physical characteristics of the rock, including acoustic impedance and porosity. Carbonate reservoirs are recognised for their complex pore structures and heterogeneity, which present difficulties in their characterisation. The objective of this research is to predict the porosity and identify the reservoir within the dense carbonate reservoirs in Central Luconia, Sarawak. These objectives are achieved by employing a porosity and acoustic impedance cross-plot and improved precision and predictability through the integration of seismic attribute interpretation and deterministic seismic inversions. The uniqueness of our approach stems from the incorporation of various geophysical techniques to detect reservoirs that have hydrocarbon deposits. A correlation is observed between seismic inversion acoustic impedance and porosity within the zone of interest, indicating an estimated porosity range of 10–35%. The analysed area demonstrates the possibility of containing a hydrocarbon based on the observed relationship between porosity and impedance, as well as the outcomes of the inversion analysis.

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Machine Learning Applications of 4D Seismic in Carbonate: Case Study Offshore Abu Dhabi

Mahgoub

Bashir

Berry

2022

Day 3 Wed, November 02, 2022

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Seismic 4D analysis is a model for integrating different disciplines in the oil and gas industry, such as seismic, petrophysics, reservoir engineering, and production engineering. Two 3D seismic surveys were conducted in the studied area with low repeatability of the recordings: the baseline survey in 1994 and the monitoring survey in 2014. A full 4D seismic co-processing of the baseline and monitor surveys was performed for both surveys starting with the field tapes. The 4D seismic co-processing improved poor seismic acquisition repeatability and 4D seismic attributes such as NRMS and predictability showed that. 4D time-trace shift was also performed, using the baseline survey as a reference to measure the time shifts between the baseline survey and the monitor survey at 20-year intervals. Dynamic 4D trace warping was followed by seismic 4D inversion to compare the 4D difference in the seismic inverted data with the difference in seismic amplitude. The seismic inversion helped overcome noise, multiple contamination, and differences in dynamic amplitude range between the baseline and seismic monitoring measurements. Applications of machine learning in the geosciences are growing rapidly in both processing and seismic interpretation. 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 the adjacent seismic data. We then tested the Deep Forward Neural Network (DFNN) on six wells to adequately train and validate the machine learning approach using baseline seismic inversion data and monitoring data. The objective of trying such a supervised machine learning approach was to predict the density and porosity of both the baseline seismic data and the monitoring seismic data to verify the accuracy of the 4D seismic inversion.

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4D seismic co-processing pre-stack depth migration for scarce acquisition repeatability

Cited by 3 publications

References 26 publications

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

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

Cohesive approach for determining porosity and P-impedance in carbonate rocks using seismic attributes and inversion analysis

Machine Learning Applications of 4D Seismic in Carbonate: Case Study Offshore Abu Dhabi

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