Application of Artificial Intelligence in the Oil and Gas Industry

Hussain, Muhammad; Alamri, Aeshah; Zhang, Tieling; Jamil, Ishrat

doi:10.1007/978-3-031-50300-9_19

Cited by 4 publications

References 57 publications

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Hydrocarbon prospective study using seismic inversion and rock physics in an offshore field, Niger Delta

Falade,

Amigun,

Abiola

2024

Discov Geosci

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This study integrates seismic inversion and rock physics techniques to evaluate the hydrocarbon potential of an offshore field in the Niger Delta. Five wells revealed three reservoir sands with favourable reservoir properties, including gross thickness (49.2–81.4 m), porosity (0.18–0.2), permeability (565–1481 mD), and water saturation (0.16–0.54). A robust wavelet extraction process was implemented to guide seismic inversion, and a well log-centric approach was employed to validate the resulting acoustic impedance data. Rock physics analysis established correlations between acoustic impedance (Zp), porosity, fluid content, and lithology, enabling the identification of hydrocarbon-filled sands, brine-saturated sands, and shales. These relationships enabled the discrimination of hydrocarbon-filled sands [5000–8000 (m/s)(g/cc)], from brine-saturated sands [5600–8400 (m/s)(g/cc)], and shales [5000–9000 (m/s)(g/cc)] within the inverted seismic data. The inverted acoustic impedance section showed a general increase with depth, reflecting the typical compaction effects in the Niger Delta. Analysis of the impedance distribution across horizon time slices revealed prospective zones with low impedance values [below 6300 (m/s)(g/cc)], particularly in horizons 1 and 2. These newly identified zones exhibit the strongest potential for hydrocarbon accumulation and warrant further investigation. This study demonstrates the effectiveness of using well log and rock physics constrained seismic inversion for hydrocarbon exploration in an offshore field in the Niger Delta.

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Hydrocarbon prospective study using seismic inversion and rock physics in an offshore field, Niger Delta

Falade,

Amigun,

Abiola

2024

Discov Geosci

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The Used of an Intelligent Data Assimilation Protocol for Plume Characterization of CO2 Sequestration in Saline Aquifers

Sun,

Zhang

2024

Adipec

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The gas plume characterization of CO2 sequestration processes is a typical inverse problem, the solution of which could include inevitable non-unique solutions and uncertainties. This work aims at structuring a plume characterization protocol via coupling deep neural network models and ensembled Kalman Filter algorithm by analyzing injection and monitoring well bottomhole pressure data. Considering the multiple sequestration mechanisms, the output of the model includes the spatiotemporal evolution of free gas plume and mineralization profiles. In this study, one inverse model and two types of forward-looking models are developed. The inverse model predicts geological characteristics using field pressure data as input. The forward-looking models aim to simulate pressure responses and the evolution of the gas plume. Initially, input data is processed through the inverse model to estimate the spatial distribution of geological properties. The forward-looking models couple the ensemble Kalman filter to refine the predictions made by the inverse model. After the predictions are aligned with the field data, these models proceed to forecast the distribution of the gas plume in different form of presences. The proposed methodology was evaluated using an ideal case and a field case using the geological data collected from a real aquifer. The primary source of uncertainty in gas plume characterization stems from the inherent non-uniqueness of solutions to inverse problems. This methodology utilizes the heterogeneities in petrophysical properties, as predicted by the inverse model, as an intermediary variable. The incorporation of an AI-assisted data assimilation protocol substantially reduces this uncertainty by refining the outcomes from the inverse model. The findings indicate that deep neural networks models adapted from an auto encode architecture derived from the U-net are effective for image-to-image regression predictions using static heterogeneity property distributions as inputs. This approach leads to the training of an expert system capable of forecasting the varied spatiotemporal dynamics of carbon species within saline aquifers. After injection ceases and the pressure transient spreads, the gas plume evolution becomes less sensitive to changes in the injection well bottomhole pressure. It highlights the importance of strategically placing monitoring wells to effectively oversee long-term gas migration and plume characteristics. The integration of expert systems with ensemble Kalman filters successfully delineates the spatial and temporal evolution of CO2 plumes by analyzing pressure data from injection and monitoring wells. This method, in contrast to traditional CO2 plume inversion techniques, demands fewer and more affordable data inputs. As a result, it offers cost-effective and precise CO2 plume characterization considering different forms of presences.

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Development of Machine Learning-Based Production Forecasting for Offshore Gas Fields Using a Dynamic Material Balance Equation

Hyoung,

Lee,

Han

2024

Energies

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Offshore oil and gas fields pose significant challenges due to their lower accessibility compared to onshore fields. To enhance operational efficiency in these deep-sea environments, it is essential to design optimal fluid production conditions that ensure equipment durability and flow safety. This study aims to develop a smart operational solution that integrates data from three offshore gas fields with a dynamic material balance equation (DMBE) method. By combining the material balance equation and inflow performance relation (IPR), we establish a reservoir flow analysis model linked to an AI-trained production pipe and subsea pipeline flow analysis model. We simulate time-dependent changes in reservoir production capacity using DMBE and IPR. Additionally, we utilize SLB’s PIPESIM software to create a vertical flow performance (VFP) table under various conditions. Machine learning techniques train this VFP table to analyze pipeline flow characteristics and parameter correlations, ultimately developing a model to predict bottomhole pressure (BHP) for specific production conditions. Our research employs three methods to select the deep learning model, ultimately opting for a multilayer perceptron (MLP) combined with regression. The trained model’s predictions show an average error rate of within 1.5% when compared with existing commercial simulators, demonstrating high accuracy. This research is expected to enable efficient production management and risk forecasting for each well, thus increasing revenue, minimizing operational costs, and contributing to stable plant operations and predictive maintenance of equipment.

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Application of Artificial Intelligence in the Oil and Gas Industry

Cited by 4 publications

References 57 publications

Hydrocarbon prospective study using seismic inversion and rock physics in an offshore field, Niger Delta

Hydrocarbon prospective study using seismic inversion and rock physics in an offshore field, Niger Delta

The Used of an Intelligent Data Assimilation Protocol for Plume Characterization of CO2 Sequestration in Saline Aquifers

Development of Machine Learning-Based Production Forecasting for Offshore Gas Fields Using a Dynamic Material Balance Equation

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