4D seismic opportunity: from feasibility to reservoir characterization – a case study offshore West Africa

Webb, B.; Rizzetto, Catia; Pirera, F.; Paparozzi, E.; Milluzzo, Vincenzo; Mastellone, Daniela; Marchesini, Marco; Colombi, N.; Buia, Michele; Bertarini, Massimiliano

doi:10.3997/1365-2397.n0016

Cited by 3 publications

(1 citation statement)

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“…Petro-elastic model (PEM) and 1D time-lapse modeling of the research area support the 4D signal from well production and injection data. A fluid substitution tool performed substitution of fluids from an initial reservoir state to a new state and examined the effects of this fluid substitution on the elastic properties of the reservoir [23].…”

Section: Methodsmentioning

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: Methodsmentioning

confidence: 99%

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

et al. 2022

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Seismic modelling for reservoir studies: a comparison between convolutional and full‐waveform methods for a deep‐water turbidite sandstone reservoir

Amini

MacBeth

Shams

2020

Geophysical Prospecting

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Two seismic modelling approaches, that is, two‐dimensional pre‐stack elastic finite‐difference and one‐dimensional convolution methods, are compared in a modelling exercise over the fluid‐flow simulation model of a producing deep‐water turbidite sandstone reservoir in the West of Shetland Basin. If the appropriate parameterization for one‐dimensional convolution is used, the differences in three‐dimensional and four‐dimensional seismic responses from the two methods are negligible. The key parameters to ensure an accurate seismic response are a representative wavelet, the distribution of common‐depth points and their associated angles of incidence. Conventional seismic images generated by the one‐dimensional convolutional model suffer from lack of continuity because it only accounts for vertical resolution. After application of a lateral resolution function, the convolutional and finite‐difference seismic images are very similar. Although transmission effects, internal multiples and P‐to‐S conversions are not included in our convolutional modelling, the subtle differences between images from the two methods indicates that such effects are of secondary nature in our study. A quantitative comparison of the (normalized root‐mean‐square) amplitude attributes and waveform kinematics indicates that the finite‐difference approach does not offer any tangible benefit in our target‐oriented seismic modelling case study, and the potential errors from one‐dimensional convolution modelling are comparatively much smaller than the production‐induced time‐lapse changes.

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A time-lapse case study in West Africa: Integrating disciplines for a complete reservoir study and field management

Webb¹,

Salerno²,

Rizzetto³

et al. 2020

The Leading Edge

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Development and management of deepwater oil fields requires material investment and must be supported by application of quality technological solutions and tools. To maximize the advantages that can derive from these, it is essential to integrate technologies and disciplines throughout the field cycle. This integration makes it possible to identify and fix issues early and seize business opportunities quickly. We describe the activity carried out on a field characterized by a high-quality oil-bearing clastic reservoir located in a deepwater belt along a West African continental passive margin. We highlight how geoscience specialists from both geologic and geophysical branches have contributed to construction of the static and dynamic reservoir model, field monitoring, model updating, and operations optimization. The wide geologic and geophysical experience in the area made it possible to build a petroelastic model and a sedimentological model that were integrated in the population of a geocellular model. Seismic data also were used to support reservoir monitoring of the producing field. A dedicated 4D seismic project helped our understanding of fluid movement and fault behaviors in the field. The 4D information was interpreted and shared within the integrated team with the goal of identifying further business opportunities and driving decision making over our area of interest.

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4D seismic opportunity: from feasibility to reservoir characterization – a case study offshore West Africa

Cited by 3 publications

References 0 publications

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

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

Seismic modelling for reservoir studies: a comparison between convolutional and full‐waveform methods for a deep‐water turbidite sandstone reservoir

A time-lapse case study in West Africa: Integrating disciplines for a complete reservoir study and field management

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