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.

Time-lapse (4D) seismic processing is routinely used to monitor hydrocarbon reservoir production. Seismic reflections are sensitive to formation pressure and fluid content. This means that repeated seismic surveys can theoretically detect pressure changes and fluid changes associated with field production. These measurements can help optimize production strategy and identify areas where hydrocarbons have been bypassed. However, the seismic signal associated with such changes can be negligible, especially in heterogeneous carbonate reservoirs. To measure this 4D signal, the seismic acquisition must be repeated. Data vintages should be processed together to minimize differences unrelated to production. Repeatability of data acquisition is sometimes impossible to achieve in the Middle East due to environmental changes (e.g., dunes, currents, field facilities). Due to high cost and inadequate sampling, attempts to permanently bury seismic sources and receivers have failed.In this study, least-square pre-stack depth migration (LSM) co-processing was applied to remove the influence of survey design on the final image and pinching mark compared to conventional Kirchhoff pre-stack depth migration (KPSDM). The 4D physical, geometric, and seismic attributes are analyzed in the field as key diagnostic tools to evaluate the probability of a sighted 4D difference independent of two different acquisition geometries. The 4D analysis was performed on two case studies offshore Abu Dhabi, to determine which workflow and algorithm are most likely to ensure that the complete and optimal 4D processing sequence relaxes the need for seismic acquisition repeatability.