Identification and petrophysical evaluation of thinly bedded low-resistivity pay reservoir in the Niger Delta

Mode, Ayonma Wilfred; Anyiam, Okwudiri A.; Aghara, I. K.

doi:10.1007/s12517-014-1348-4

Cited by 14 publications

(11 citation statements)

References 2 publications

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“…These may result in inaccurate assessment of reservoir compartmentalization which could cause ambiguity in estimating gross rock volume and hydrocarbon volume (Shepherd 2009). However, all the estimated petrophysical parameters are in line with those reported by other researchers in Niger Delta (Adeoye and Enikanselu 2009;Opara 2010;Mode et al 2014); they formed the essential parameters to quantitatively determine the stock tank oil initially in place (STOIIP) in the field.…”

Section: Petrophysical Evaluationsupporting

confidence: 88%

Hydrocarbon resource evaluation using combined petrophysical analysis and seismically derived reservoir characterization, offshore Niger Delta

Oyeyemi

Olowokere

Aizebeokhai

2017

J Petrol Explor Prod Technol

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Subsurface characterization and hydrocarbon resource evaluation were conducted using integrated well logs analysis and three-dimensional (3D) seismic-based reservoir characterization in an offshore field, western Niger Delta basin. Reservoir sands R1-R4 were delineated, mapped and quantitatively evaluated for petrophysical characteristics such as net-to-gross, volume of shale, water saturation, bulk water volume, porosity, permeability, fluid types and fluid contacts (GOC and OWC). The volume attributes aimed at extracting features associated with hydrocarbon presence detection, net pay evaluation and porosity estimation for optima reservoir characterization. Neural network (NN)-derived chimney properties prediction attribute was used to evaluate the integrity of the delineated structural traps. Common contour binning was employed for hydrocarbon prospect evaluation, while the seismic coloured inversion was also applied for net pay evaluation. The petrophysical properties estimations for the delineated reservoir sand units have the porosity range from 21.3 to 30.62%, hydrocarbon saturation 80.70-96.90 percentage. Estimated resistivity R t , porosity and permeability values for the delineated reservoirs favour the presence of considerable amount of hydrocarbon (oil and gas) within the reservoirs. Amplitude anomalies were equally used to delineate bright spots and flat spots; good quality reservoirs in term of their porosity models, and fluid content and contacts (GOC and OWC) were identified in the area through common contour binning, seismic colour inversion and supervised NN classification.

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Section: Petrophysical Evaluationsupporting

confidence: 88%

Hydrocarbon resource evaluation using combined petrophysical analysis and seismically derived reservoir characterization, offshore Niger Delta

Oyeyemi

Olowokere

Aizebeokhai

2017

J Petrol Explor Prod Technol

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“…The excavation effect of neutron logging is an important feature of gas layer identification. The difference between the neutron and density porosities (

{\varphi }_{D-N}

) is often used as an indicator to identify the gas layers 5,29 …”

Section: Identification Of Low‐resistivity Gas Layer In the He 8 Sect...mentioning

confidence: 99%

“…According to different conductive mechanisms, low resistivity in hydrocarbon reservoirs can be caused by two reasons: additional conductivity caused by skeletal components and additional conductivity caused by fluid in the pores 1–3 . Additional conductivity of skeletal components includes clay mineral conductivity, 4 heavy mineral conductivity, and argillaceous lamina or thin interlayer conductivity 5,6 . Additional conductivity caused by pore fluid can be attributed to high irreducible water saturation due to a complex pore structure, 7–9 high salinity drilling mud invasion, 10 relatively high salinity of formation water, 11 and high in‐wall‐bound water content caused by strongly hydrophilic particles and rocks 12 .…”

Section: Introductionmentioning

confidence: 99%

Genetic analysis and identification of low‐resistivity gas reservoirs of southwestern Sulige gas field in China

Yang

Zhang

et al. 2022

Energy Science & Engineering

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Sulige gas field is the largest natural gas producing area in China. Due to the "water blocking" effect, the gas-water layer significantly influences the development of gas reservoirs. The existence of low-resistivity gas layers in the He 8 member of the Shihezi Formation in the southwestern Sulige gas field makes it challenging to distinguish the gas layers from the gas-water layers using conventional identification methods. To effectively identify the low-resistivity gas layers, their genetic mechanisms are analyzed by studying their lithology and pore structural characteristics based on the well logging and core experimental data. The low-resistivity gas layers are main affected by three causes: (1) electrical conductivity of argillaceous laminae in sandstone,(2) additional conductivity of clay minerals, and (3) high bound water saturation caused by the development of micropores and clay minerals in sandstone. Herein, to effectively distinguish the low-resistivity gas layer from the gas-water layer based on the genetic mechanism of the low-resistivity gas layer, first, a gas-bearing index was constructed to characterize the gas-bearing properties of the reservoir using the neutron logging and density logging curves after eliminating the influence of the dispersed shale. Second, a model to calculate the bound water saturation was constructed by selecting sensitive parameters with respect to the causes of bound water. Then, two plots, namely the gas-bearing index-natural gamma relative value cross-plot and the bound water content-porosity cross-plots, were constructed using the gas testing data, and the identification standard of the low-resistivity gas and gas-water layers was established. The interactive identification of the two cross-plots effectively distinguished the low-resistivity gas layer from the gaswater layer, thereby providing a basis for understanding the distribution of gas and water in the southwestern Sulige gas field, which may guide further exploration and the deployment of the development well pattern.

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“…Carrying out the research on logging interpretation and evaluation method of resistivity low-contrast oil pays has become the most practical choice to supplement conventional oil resources and reduce oilfield exploration cost 1 , 2 , 27 . The resistivity low-contrast oil pay has the characteristics of little difference in porosity and resistivity logging response from water layer, and the oil saturation of resistivity low-contrast oil layer is relatively low 3 , 4 . At present, low porosity and low permeability reservoirs represented by tight sandstone has become the main battlefield to ensure the supply of oil and gas resources 5 .…”

Section: Introductionmentioning

confidence: 99%

Log interpretation method of resistivity low-contrast oil pays in Chang 8 tight sandstone of Huanxian area, Ordos Basin by support vector machine

Bai

Tan

Shi

et al. 2022

Sci Rep

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Resistivity low-contrast oil pays are a kind of unconventional oil resource with no obvious difference in physical and electrical properties from water layers, which makes it difficult to be identified based on the characteristics of the geophysical well logging response. In this study, the support vector machine (SVM) technology was used to interpret the resistivity low-contrast oil pays in Chang 8 tight sandstone reservoir of Huanxian area, Ordos Basin. First, the input data sequences of logging curves were selected by analyzing the relationship between reservoir fluid types and logging data. Then, the SVM classification model for fluid identification and SVR regression model for reservoir parameter prediction were constructed. Finally, these two models were applied to interpret the resistivity low-contrast oil pays in the study area. The application results show that the fluid recognition accuracy of the SVM classification model is higher than that of the logging cross plot method, back propagation neural network method and radial basis function neural network method. The calculation accuracy of permeability and water saturation predicted by the SVR regression model is higher than that based on the experimental fitting model, which indicates that it is feasible to carry out logging interpretation and evaluation of the resistivity low-contrast oil pays by the SVM method. The research results not only provide an important reference and basis for the review of old wells but also provide technical support for the exploration and development of new strata.

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Identification and petrophysical evaluation of thinly bedded low-resistivity pay reservoir in the Niger Delta

Cited by 14 publications

References 2 publications

Hydrocarbon resource evaluation using combined petrophysical analysis and seismically derived reservoir characterization, offshore Niger Delta

Hydrocarbon resource evaluation using combined petrophysical analysis and seismically derived reservoir characterization, offshore Niger Delta

Genetic analysis and identification of low‐resistivity gas reservoirs of southwestern Sulige gas field in China

Log interpretation method of resistivity low-contrast oil pays in Chang 8 tight sandstone of Huanxian area, Ordos Basin by support vector machine

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