3-D static modelling of lateral heterogeneity using geostatistics and artificial neural network in reservoir characterisation of “P” field, Niger Delta

Fajana, Akindeji Opeyemi

doi:10.1080/20909977.2020.1727674

Cited by 6 publications

(1 citation statement)

References 16 publications

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“…This process referred to as reservoir characterization depends mostly on the prediction of lithology and fluid discrimination. A major challenge in hydrocarbon exploration lies in the need for proper mapping of the reservoir and its characterization to determine the economic value of such a field [1]. Although several techniques exist in the characterization of hydrocarbon field in search for probable reservoir saturated with commercial hydrocarbon for exploration, Gassmann relations are the most commonly and widely used equation worldwide.…”

Section: Introductionmentioning

confidence: 99%

Fluid substitution modelling and derived rock attribute-based discriminations of hydrocarbon and lithology: a case study of arcuate Niger Delta Depobelt, Nigeria

et al. 2020

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Fluid substitution modelling was carried out to discriminate the formation fluids in X-field. The study employed 3D seismic volume and well logs from two exploratory wells. Two reservoir sands, labelled HD3000 and HD5000, were delineated based on the well signatures of gamma ray and resistivity logs. These reservoir zones are thicker in well X2 than in well X1. Saturations of fluid in the delineated reservoirs were varied using 50% for brine, gas and oil scenarios. The main goal of the study is to distinguish the formation fluid present in the reservoirs based on the crossplot of the fluid replacement modelling scenarios. The crossplot of P-impedance versus S-impedance attributes revealed separation in clusters representing the scenarios for the different fluid saturations in the reservoirs. A clear distinction was deciphered between fluid and lithology in the field based on the clusters of crossplots. The synthetic result revealed positive increase in amplitude at the reservoir tops for brine saturation, while positive decrease in amplitude were depicted in the case of oil and gas saturations. P-wave decreased more with 50% brine saturation when compared to the decrease with 50% gas/oil saturation scenarios. The inverted P-impedance amplitude slice reveals four (4) zones labelled H1, H2 H3 and H4 suspected to be saturated with probable hydrocarbon accumulation based on the low impedance observed around the inferred zones.

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Section: Introductionmentioning

confidence: 99%

Fluid substitution modelling and derived rock attribute-based discriminations of hydrocarbon and lithology: a case study of arcuate Niger Delta Depobelt, Nigeria

et al. 2020

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Comparison of modified Waxman-Smith algorithms and Archie models in prospectivity analysis of saturations in shaly-sand reservoirs. A case study of Pennay field, Niger-Delta

2021

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Characterization of deep water turbidite channels and submarine fan lobes using artificial intelligence; Case study of Frem Field deep offshore Niger Delta

Obafemi

Oyedele

Omeru

et al. 2020

Journal of African Earth Sciences

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3-D static modelling of lateral heterogeneity using geostatistics and artificial neural network in reservoir characterisation of “P” field, Niger Delta

Abstract: View related articlesView Crossmark data Citing articles: 4 View citing articles 3-D static modelling of lateral heterogeneity using geostatistics and artificial neural network in reservoir characterisation of "P" field, Niger Delta

Cited by 6 publications

References 16 publications

Fluid substitution modelling and derived rock attribute-based discriminations of hydrocarbon and lithology: a case study of arcuate Niger Delta Depobelt, Nigeria

Fluid substitution modelling and derived rock attribute-based discriminations of hydrocarbon and lithology: a case study of arcuate Niger Delta Depobelt, Nigeria

Comparison of modified Waxman-Smith algorithms and Archie models in prospectivity analysis of saturations in shaly-sand reservoirs. A case study of Pennay field, Niger-Delta

Characterization of deep water turbidite channels and submarine fan lobes using artificial intelligence; Case study of Frem Field deep offshore Niger Delta

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