Development and Identification of Petrophysical Rock Types for Effective Reservoir Characterization: Case Study of the Kristine Field, Offshore Sabah

Barach, Bior Atem Bior; Jaafar, Mohd Zaidi; Gaafar, Gamal Ragab; Agi, Augustine

doi:10.1007/s11053-021-09851-3

Cited by 12 publications

(5 citation statements)

References 22 publications

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“…In the bottom of the last zone, a gradational variation from the Kazhdumi shale to Sarvak limestone indicates a regressive environmental condition. The porosity data variation indicated that the reservoir quality would improve from the base to the top 12,28,52,55 .…”

Section: 2general Stratigraphymentioning

confidence: 99%

Electrical facies of the Asmari Formation in the Mansouri oilfield, an application of multi-resolution graph-based and artificial neural network clustering methods

Eftekhari,

Memariani,

Maleki

et al. 2023

Preprint

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Electrofacies analysis conducted the distribution effects throughout the reservoir despite the difficulty of characterizing stratigraphic relationships. Clustering methods quantitatively define the reservoir zone from non-reservoir considering electrofacies. Asmari Formation is the most significant reservoir of the Mansouri oilfield in SW Iran, generally composed of carbonate and sandstone layers. The stratigraphical study is determined by employing 250 core samples from one exploratory well in the studied field. Five zones with the best reservoir quality in zones 3 and 5 containing sandstone/shale are determined. Moreover, multi-resolution graph-based and artificial neural network clustering involving six logs are employed. Utilizing Geolog software, an optimal model with eight clusters with better rock separation is obtained. Eventually, five electrofacies with different lithological compositions and reservoir conditions are identified and based on lithofacies describing thin sections, sandstone, and shale in zones 3 and 5 show high reservoir quality. According to the depth related to these zones, most of the facies that exist in these depths include sandstone and dolomite facies, and this is affected by the two factors of the primary sedimentary texture and the effect of the diagenesis process on them. Results can compared to the clustering zone determination in other nearby sandstone reservoirs without cores.

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Section: 2general Stratigraphymentioning

confidence: 99%

Electrical facies of the Asmari Formation in the Mansouri oilfield, an application of multi-resolution graph-based and artificial neural network clustering methods

Eftekhari,

Memariani,

Maleki

et al. 2023

Preprint

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Section: General Stratigraphymentioning

confidence: 99%

Determining the electrical facies utilizing multi-resolution graph-based and artificial neural network clustering methods in an Oilfield, SW Iran

Eftekhari,

Memariani,

Maleki

et al. 2023

Preprint

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Asmari Formation is the most significant reservoir of the Mansouri oil field in SW Iran. This Formation is generally composed of carbonate lithologic but has sandstone layers in some zones. It is also the youngest reservoir rock in Iran. Cross-lithology diagrams determined the lithology of the Asmari Formation by applying 280 core samples from one drilled well in the studied reservoir, and a combination of lime, dolomite, anhydrite, and sandstone with shaley layers was estimated. Also, five zones were identified with different geological/reservoir conditions, with the best reservoir quality in zones 3 and 5 with sandstone/shale lithology. Furthermore, this study determines multi-resolution graph-based clustering (MRGC) and artificial neural network (ANN) facies clustering methods. For this purpose, the combined log readings of DT, RHOB, GR, Sw, NPHI, and PHIE were used. Lithology was evaluated and estimated in each sequence using corrected and edited logs and lithology cross-sections. After applying the Geolog software, an optimal model with 8 clusters (facies) with better separation of rock species was obtained. Finally, five facies with different lithological compositions and reservoir conditions were identified. The information obtained from determining electrical facies by clustering method defines the reservoir zone from non-reservoir and qualitatively (good, medium, and poor).

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“…Nevertheless, estimation of these reservoir properties is a cumbersome process due to heterogeneous nature of the subsurface (pore space and reservoir geometry) [215]. Consequently, conventional formation evaluation based on well logs to establish a statistically significant correlation between the reservoir storage and fluid flow characteristics cannot provide enough information for deriving reservoir characteristics [216]. For instance, lateral variation in sand continuity in carbonate reservoir provides inaccurate prediction of permeability far away from the well location.…”

Section: ) Reservoir Characterizationmentioning

confidence: 99%

“…For instance, lateral variation in sand continuity in carbonate reservoir provides inaccurate prediction of permeability far away from the well location. For instance, when the number of wells is less, estimation using well logs do not provide satisfactory results [216]. AI has been used to circumvent these problems by integrating ML with an expert system to predict depositional facies, which can be validated with facies interpretation from conventional cores in test wells [217].…”

Section: ) Reservoir Characterizationmentioning

confidence: 99%

A Survey on Industry 4.0 for the Oil and Gas Industry: Upstream Sector

et al. 2021

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The market volatility in the oil and gas (O&G) sector, the dwindling demand for oil due to the impact of COVID-19, and the push for alternative greener energy are driving the need for innovation and digitization in the O&G industry. This has attracted research interest from academia and the industry in the application of industry 4.0 (I4.0) technologies in the O&G sector. The application of some of these I4.0 technologies has been presented in the literature, but the domain still lacks a comprehensive survey of the application of I4.0 in the O&G upstream sector. This paper investigates the state-of-the-art efforts directed toward I4.0 technologies in the O&G upstream sector. To achieve this, first, an overview of the I4.0 is discussed followed by a systematic literature review from an integrative perspective for publications between 2012-2021 with 223 analyzed documents. The benefits and challenges of the adoption of I4.0 have been identified. Moreover, the paper adds value by proposing a framework for the implementation of I4.0 in the O&G upstream sector. Finally, future directions and research opportunities such as framework, edge computing, quantum computing, communication technologies, standardization, and innovative areas related to the implementation of I4.0 in the upstream sector are presented. The findings from this review show that I4.0 technologies are currently being explored and deployed for various aspects of the upstream sector. However, some of the I4.0 technologies like additive manufacturing and virtual reality are least explored.INDEX TERMS Artificial intelligence (AI), cyber-physical systems, digital-twin, framework, oil and gas (O&G), industry revolution 4.0 (IR 4.0), industry 4.0 (I4.0), internet of things (IoT), simulation, upstream sector

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Development and Identification of Petrophysical Rock Types for Effective Reservoir Characterization: Case Study of the Kristine Field, Offshore Sabah

Cited by 12 publications

References 22 publications

Electrical facies of the Asmari Formation in the Mansouri oilfield, an application of multi-resolution graph-based and artificial neural network clustering methods

Electrical facies of the Asmari Formation in the Mansouri oilfield, an application of multi-resolution graph-based and artificial neural network clustering methods

Determining the electrical facies utilizing multi-resolution graph-based and artificial neural network clustering methods in an Oilfield, SW Iran

A Survey on Industry 4.0 for the Oil and Gas Industry: Upstream Sector

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