The world’s largest continuous 3D onshore and offshore seismic survey sets ambitious quality and turnaround targets

Cambois, Guillaume; Mesaabi, Saif Al; Casson, George Ainslie; Cowell, James; Mahgoub, Mohamed; Kobaisi, Abdulla Al

doi:10.1190/segam2019-3214663.1

Cited by 7 publications

(4 citation statements)

References 2 publications

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“…To ensure high-quality depth imaging, nearly 10,000-30,000 sensors are expected to be deployed over areas as large as 150-300 km 2 in OBS acquisition [5]. A sweep typically lasts for a duration of 8-12 s, called the sweep length.…”

Section: Marine Seismic Acquisitionmentioning

confidence: 99%

“…Real-time acquisition at the SV can enable scenarios where a group of AUVs is guided to a desired location and depth [14] affects the quality of the seismic data but also hampers the movement of the SV. Furthermore, the survey is halted when the presence of any animals (that depend on acoustic communication) is detected within the survey area [1], [5].…”

Section: A Wireless Network Requirements For Marine Seismic Acquisitionmentioning

confidence: 99%

“…The application of wireless buoy networks in OBS acquisition has received little attention, where a few works [3], [4] have provided solutions for the delivery of small amounts of quality-control (QC) information and not the entirety of the seismic dataset in real-time. This arises from the fact that unlike typical low-rate sensor networks, data is generated at a rate of several Gigabits per second across an area of 150 − 300 km 2 [5], [6]! Moreover, a description of the specific protocol and any power-saving mechanism has not been provided in [3], [4].…”

Section: Introductionmentioning

confidence: 99%

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Wi-Buoy: An Energy-Efficient Wireless Buoy Network for Real-Time High-Rate Marine Data Acquisition

Reddy

Stuber

2021

IEEE Access

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The next frontier of maritime networking will see the deployment of large-scale buoybased mesh networks, with an equal emphasis on both high-speed data transfer and energy-efficiency. One such challenging application is the operation of maritime seismic surveys for oil/gas exploration and academic studies. Large amounts of seismic data are generated at a rate of several Gigabits per second, by nearly 10,000-30,000 seismic sensors that are deployed on the seabed across an area of several square kilometers in offshore oceanic environments. The task of monitoring existing reservoirs and identifying new oil and gas deposits require subsurface images of superior quality, which in turn are dependent on high-quality data for processing. Wireless technology can unlock real-time data transfer for rapid image-viewing, enhanced productivity, and reduced logistical costs. This interdisciplinary article outlines the challenges of marine seismic acquisition and the design of a buoy-based wireless backhaul network for high-rate data transfer over the ocean surface. Based on off-the-shelf IEEE 802.11 systems, a standards-compliant wireless buoy network architecture called Wi-buoy is proposed for real-time, scalable, and energy-efficient data delivery. In order to attain optimal power conservation, a Buoy-Based Power-Saving Backhaul (B-PSB) scheme is also proposed for specifying the operating parameters across all layers of the protocol stack. Essential aspects of the marine propagation environment are reviewed, and the performance of the proposed system is evaluated as a function of the antenna height, wind speed, compression ratio, and various flavors of the IEEE 802.11 standard. Furthermore, the use of Autonomous Underwater Vehicles (AUVs) is analyzed as an integral component of upcoming high-speed buoy-based networks in the maritime environment.

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Section: Marine Seismic Acquisitionmentioning

confidence: 99%

Section: A Wireless Network Requirements For Marine Seismic Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wi-Buoy: An Energy-Efficient Wireless Buoy Network for Real-Time High-Rate Marine Data Acquisition

Reddy

Stuber

2021

IEEE Access

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“…The acquisition began in late 2018 and is expected to be completed by the end of 2022, with final images delivered two years later. This is an extremely tight schedule considering that nearly 90,000 km2 need to be covered [1].…”

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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A Machine-Learning Approach to Removing Highly Variable Surface-Waves for a Large Shallow-Water Survey Offshore Abu Dhabi

Badreia

Cambois

Mahgoub

et al. 2020

Day 1 Mon, November 09, 2020

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A large, full-azimuth, shallow-water ocean-bottom node (OBN) survey offshore Abu Dhabi revealed an intriguing azimuth-dependent linear noise pattern. The mud rolls and guided waves appear to curve along surface and near-surface features. Although we have yet to fully understand the mechanism that causes this phenomenon, we need to accurately and effectively eliminate these linear noises to meet the tight processing deadline. In view of the huge amount of data involved, we implemented a data-driven machine learning (ML) technology. Mud rolls and guided waves are dispersive in nature. The standard way to remove them is to compute the dispersion curve (DC) (phase velocity as a function of frequency) from the data, model the corresponding mode, and subtract the model from the data in an adaptive way. In our case, the linear noise patterns are not only related to the azimuth angle of acquisition, but they also vary spatially, which makes removing them even more challenging. Since we cannot manually select the dispersion curves (DC) given the size of the data, we implemented an ML algorithm to classify the noise pattern and design the best noise removal operator for each category. The remaining outliers can then be resolved using customized solutions. In order to transform seismic data analysis into a pattern recognition problem, we need to generate attribute maps. In our case, one of the attribute map is the dispersion curve (DC), which has been calculated from the data. The maps are used as input to the ML stream which identifies classes by unsupervised K-means clustering analysis. Another attribute used is the root-mean- square (RMS) amplitude calculated over a fixed time window for each receiver gather. The corresponding maps are used as the input to the ML flow, which involves two-steps: first reduce the dimensionality of the input data by performing a principal component analysis (PCA), then identify classes through unsupervised K-mean clustering analysis. The generated class determines the common linear noise behavior and its spatial distribution group, thereby effectively driving the linear noise attenuation parameter setup.

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The world’s largest continuous 3D onshore and offshore seismic survey sets ambitious quality and turnaround targets

Cited by 7 publications

References 2 publications

Wi-Buoy: An Energy-Efficient Wireless Buoy Network for Real-Time High-Rate Marine Data Acquisition

Wi-Buoy: An Energy-Efficient Wireless Buoy Network for Real-Time High-Rate Marine Data Acquisition

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

A Machine-Learning Approach to Removing Highly Variable Surface-Waves for a Large Shallow-Water Survey Offshore Abu Dhabi

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