Deterministic and Stochastic Seismic Inversion, a Comparison Study Applied to Capture Major Heterogeneities and its Associated Uncertainties during Early Characterization Stage in Upper Jurassic Reservoirs in the UAE

Aamir, Muhammad; Figuera, L.G.; Hosani, Khaled Al; Singh, Maniesh; Ahmad, Mula Nur; Solaiman, Tarek; Qallabi, Salah Al; Araque, Arcides; Nnamdi, Dike; Krissat, Widad; El-Mawla, Walid; Aly, Omar; Elsayed, Raghad; Mukherjee, Anirban; Nyein, G.; Havelia, Khushboo; Nafie, Tarek

doi:10.2118/188404-ms

Day 1 Mon, November 13, 2017 2017

DOI: 10.2118/188404-ms

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Deterministic and Stochastic Seismic Inversion, a Comparison Study Applied to Capture Major Heterogeneities and its Associated Uncertainties during Early Characterization Stage in Upper Jurassic Reservoirs in the UAE

Muhammad Aamir

L.G. Figuera

Khaled Al Hosani

et al.

Abstract: The studied Fields cover about 1900 sq km covering fourteen wells in Abu Dhabi, UAE. It was discovered in 1962 and developed by Abu Dhabi Petroleum Company. The objective of the current study is to illustrate the comparison between prestack stochastic & prestack deterministic seismic inversion results to better understand vertical and lateral heterogeneities of upper Jurassic reservoirs and their impact on property distribution during static modelling and further into dynamic model construction.

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Role of Well Logs and Seismic Data Conditioning in Improving the Imaging of Complex Reservoirs from Abu Dhabi

Obaid

Aamir

Nafie

et al. 2021

Day 2 Tue, November 16, 2021

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Rock physics/seismic inversion is a powerful tool that deliver information about intra-wells rocks elastic attributes and reservoir properties such as porosity, saturation and rock lithology classification. In principle, inversion is like an engine that should be fueled by proper input quality of both seismic and well data. As for the well data, sonic and density logs measure the rock properties a few inches from the borehole. Reliability of sonic transit-time and bulk density logs can be affected by large and rapid variation in the diameter and shape of the borehole cross-section, as well as the process of drilling fluid invasion. The basic assumption for acoustic well logs editing and conditioning is to use other recorded logs (not affected by bad-hole conditions) in a Multivariate-Regression Algorithm. In addition, Fluid Substitution was implemented to correct for the mud invasion that affects the acoustic and elastic properties based on the PVT data for fluid properties computation. The logs were then quality checked by multiple cross-plotting comparisons to the standard Rock-physics trends templates. As for seismic data, there are several factors affecting the quality of surface seismic data including the presence of residual noise and multiples contamination that caused improper amplitude balancing. Optimizing the seismic data processing for the inversion studies require reviewing and conditioning the seismic gathers and pre-stack volumes, guided by a deterministic seismic-to-well tie analysis after every major stage of the processing sequence. The applied processes are mainly consisting of Curvelet domain noise attenuation to attenuate residual noise. This was followed by high resolution Radon anti-multiple to attenuate residual surface multiples and Extended interbed multiple prediction to attenuate interbed multiples. In addition, Offset dependent amplitude and spectral balancing were applied to maintain the seismic amplitudes fidelity. This paper will illustrate a case from Abu Dhabi where data conditioning results improved the Hydrocarbon saturated carbonates vs brine saturated carbonate and the lithology predictions, leading to optimizing field development plans and drilling operations.

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Role of Well Logs and Seismic Data Conditioning in Improving the Imaging of Complex Reservoirs from Abu Dhabi

Obaid

Aamir

Nafie

et al. 2021

Day 2 Tue, November 16, 2021

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Estimation of Gamma Ray Values with Deep Learning from Seismic

Karimli,

Yeleskina,

Shafiyev

et al. 2023

Day 2 Wed, November 22, 2023

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Seismic inversion is pivotal in the oil and gas industry, aiding geophysicists in understanding reservoir characteristics. Despite its widespread use, the limitations of seismic data, like low resolution, introduce uncertainties that impede direct application. This study introduces an innovative method to estimate 3D Gamma Ray data from seismic reflectivity, reshaping reservoir understanding and management. The study's goal was to predict 3D Gamma Ray data and its uncertainty via seismic reflectivity. This was achieved by employing Functional Convolutional Neural Networks (FCNNs) and Continuous Wavelet Transform. By merging well data and seismic information using signal processing and deep learning, accurate forecasts of Gamma Ray values were accomplished. It's worth noting that this manuscript constitutes as a continuation of the scientific research project initiated by Shahsenov et al. in 2022. The authors describe the updated methodology, the improvements achieved in the results, as well as any alterations made to the methodological framework. The quality of prediction was validated through several statistical techniques, ensuring robustness. Blind wells were thoughtfully integrated into various analyses to facilitate a comparison between actual and predicted log values. The outcomes of these evaluations, promising in nature and showcasing notable advancements over the earlier work, are represented visually and in detail through the report. In summary, this research pioneers a seismic inversion shift and builds upon prior research (Shahsenov et al., 2022). The combination of seismic reflectivity and deep learning allows precise prediction of 3D Gamma Ray values. By combining well and seismic data, the challenge of sparse logs is addressed, empowering geophysicists for more effective reservoir management. This enhanced reservoir understanding promotes data-driven decisions in the oil and gas sector.

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Deterministic and Stochastic Seismic Inversion, a Comparison Study Applied to Capture Major Heterogeneities and its Associated Uncertainties during Early Characterization Stage in Upper Jurassic Reservoirs in the UAE

Cited by 2 publications

References 0 publications

Role of Well Logs and Seismic Data Conditioning in Improving the Imaging of Complex Reservoirs from Abu Dhabi

Role of Well Logs and Seismic Data Conditioning in Improving the Imaging of Complex Reservoirs from Abu Dhabi

Estimation of Gamma Ray Values with Deep Learning from Seismic

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