Stochastic modelling of spatial variability of petrophysical properties in parts of the Niger Delta Basin, southern Nigeria

Ebong, Ebong D.; Akpan, Anthony E.; Ekwok, Stephen E.

doi:10.1007/s13202-019-00787-2

Cited by 13 publications

(4 citation statements)

References 34 publications

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“…The lithological model of the research area was also created by combining geostatistical and geological information (Figure 7) (Ebong et al, 2020;Ebong et al, 2021). In the 3D geological model, there are two sets of sandstone reservoirs in the target layers Weizhou and Liushagang; in the southern part of the research area, sandstone is mainly distributed locally in the ascending plate of the fault, and the deep depression of the settlement center at the descending plate contains some turbidite sandstone.…”

Section: Geological Modelmentioning

confidence: 99%

Probabilistic geothermal resource assessment in Maichen Sag, south China

et al. 2022

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It is crucial for financial providers, investment groups, resource developers, and exploration companies to rate new geothermal projects in terms of resources and reserves. In general, the existing volumetric method is constrained by limited information when projects are at the early stage of development. The main objective of this study is to estimate the probabilistic potential thermal energy of the M research area in the Maichen Sag, a geothermal prospect in South China, through stochastic methodologies. The probabilistic assessment methodology provides a way to embody the uncertainty and risk in geothermal projects and to quantify the power potential in a probable range. In this study, proxy numerical models were built by combining the Experimental Design (ED) and Response Surface Methodology (RSM) with the Monte Carlo Simulation technique. An improved workflow for combined ED-RSM that uses two-level Full Factorial and Box–Behnken designs was proposed. For comparative analysis, the typical volumetric technique was also implemented in this study. The ED-RSM results show that the M area has P10, P50, and P90 reserves of 5.7 × 1014 J, 5.3 × 1014 J, and 5 × 1014 J, respectively, and these numbers from the typical volumetric method are 1.5 × 1015 J, 9 × 1014 J, and 5.1 × 1014 J, respectively. In this study, the operability, applicability, and accessibility of ED-RSM in the assessment of geothermal potential and its ability to provide a reliable output are demonstrated.

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Section: Geological Modelmentioning

confidence: 99%

Probabilistic geothermal resource assessment in Maichen Sag, south China

et al. 2022

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“…Seismic data are commonly integrated with well log data when exploring for hydrocarbon exploration (Kafisanwo et al 2018). Well log data are utilized to infer petrophysical information such as lithology, the volume of shale, net to gross ratio (NTG), permeability, porosity, water saturation, among others which provide a basis for formation evaluation (Ebong et al 2019). Generally, a rock type that has less clay content, more significant porosity and smaller irreducible water saturation tends to be of better reservoir quality in terms of storage/flow capacity (Al-Baldaini 2014).…”

Section: Introductionmentioning

confidence: 99%

“…A stochastic model is used to predict reservoir properties in the interwell space, knowing that the distance between two points has an inverse relationship with the similarity in rock properties (i.e., similarity in rock properties decreases with the increase in distance of separation) (Fournier 1995;Contreras et al 2005). Property modeling involves distributing properties between wells to match well data and realistically maintain the reservoir heterogeneity i.e., to describe variation in properties using different geostatistical methods (Ebong et al 2019). Geostatistics is not like deterministic approach because it offers many plausible results (realizations) and building of a reliable reservoir model ensures that hydrocarbon exploration and exploitation program is successful as it involves spatial distribution of reservoir information which helps improve hydrocarbon management (Edigbue et al 2015;Swinburn and Weiden 2015).…”

Section: Introductionmentioning

confidence: 99%

Characterization and geostatistical modeling of reservoirs in ‘Falad’ field, Niger Delta, Nigeria

Falade

Amigun

Makeen

et al. 2021

J Petrol Explor Prod Technol

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This research aims at characterizing and modeling delineated reservoirs in ‘Falad’ Field, Niger Delta, Nigeria, to mitigate the challenge caused by the heterogeneous nature of the reservoirs. Seismic and well log data were integrated, and geostatistics was applied to describe the reservoir properties of the interwell spaces within the study area. Four reservoirs, namely RES 1, RES 2, RES 3 and RES 4, were delineated and correlated across four wells. The reservoir properties {lithology, net to gross, porosity, permeability, water saturation} of all the delineated reservoirs mapped were determined, and two reservoirs with the best quality were picked for further analysis (surface generation and modeling) after ranking the reservoirs based on their quality. Structural interpretation of the field was carried, nine faults were mapped (F1—F9), and the fault polygon was generated. The structural model showed the area is structurally controlled with two of the major faults mapped (F1 and F3) oriented in the SW–NE direction while the other one (F4) is oriented in the NW–SE direction. A 3D grid was constructed using the surfaces of the delineated reservoirs and the reservoir properties were distributed stochastically using simple krigging method with sequential Gaussian simulation, sequential indicator simulation and Gaussian random function simulation algorithms. Geostatistical modeling used in this study has been able to give subsurface information in the areas deficient of well data as the estimated reservoir properties gotten from existing wells have been spatially distributed in the study area and will thus aid future field development while also they are used in identifying new prospect by combining property models with structural maps of the area.

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“…Therefore, a stochastic geological model should have many sister models (random realization) that exist at the same time. The random realizations of the same model generated under the same conditions may differ greatly from each other due to the variation function, and these differences merely reflect the geological uncertainty contained in the random model [6][7][8]. In addition, in terms of well pattern productivity prediction, uncertainty model analysis can help engineers choose better production strategies [9,10].…”

Section: Introductionmentioning

confidence: 99%

Relationship between well pattern density and variation function of stochastic modelling and database establishment

Wang

Zhao

Yan³

et al. 2020

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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For 3D geological modelling of oil and gas reservoirs, well pattern density is directly related to the number of samples involved in the calculation, which determines the variation function of stochastic modelling and has great impacts on the results of reservoir modelling. This paper focuses on the relationship between well pattern density and the variogram of stochastic modelling, selects the large Sulige gas field with many well pattern types as the research object, and establishes a variogram database of stochastic models for different well pattern densities. First, the well pattern in the study area is divided into three different types (well patterns A, B, and C) according to well and row space. Several different small blocks (model samples) are selected from each type of well pattern to establish the model, and their reasonable variogram values (major range, minor range and vertical range) are obtained. Then, the variogram values of all model samples with similar well pattern densities are analysed and counted, and the variogram database corresponding to each type of well pattern is established. Finally, the statistical results are applied to the modelling process of other blocks with similar well pattern density to test their accuracy. The results show that the reservoir model established by using the variation function provided in this paper agrees well with the actual geological conditions and that the random model has a high degree of convergence. This database has high adaptability, and the model established is reliable.

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Stochastic modelling of spatial variability of petrophysical properties in parts of the Niger Delta Basin, southern Nigeria

Cited by 13 publications

References 34 publications

Probabilistic geothermal resource assessment in Maichen Sag, south China

Probabilistic geothermal resource assessment in Maichen Sag, south China

Characterization and geostatistical modeling of reservoirs in ‘Falad’ field, Niger Delta, Nigeria

Relationship between well pattern density and variation function of stochastic modelling and database establishment

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