Building Petrophysical Groups in a Low Permeability Carbonate Reservoir Located in Transition Zone: A Case Study from Onshore Field in UAE

Khan, Muhammad Faisal Abdul Hameed; Maskari, Shaymaa Al; Kundu, Ashish; Voleti, Deepak Kumar; Al-Rawahi, Ali Salem

doi:10.2118/188742-ms

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…Building a robust reservoir dynamic model relies on reservoir characterization and energy management as well as reliable predictions. In order to proceed to SCAL analysis such as P c curves, it is vital to define rock types, reservoir quality, zonation, and heterogeneity through conventional core analysis and relate it to the geological facies and SCAL interpretation [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Novel Energy Lifting Approach Using J-Function and Flow Zone Indicator for Oil Fields

Tarhuni¹,

Sulaiman²,

Jaafar³

et al. 2022

Energy Engineering

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Section: Introductionmentioning

confidence: 99%

A Novel Energy Lifting Approach Using J-Function and Flow Zone Indicator for Oil Fields

Tarhuni¹,

Sulaiman²,

Jaafar³

et al. 2022

Energy Engineering

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Integrating facies analysis and geostatistical methods in an onshore oil field using petrophysical groups

Rashid

Riahi

2023

Interpretation

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Reservoir facies studies are of great importance in different stages of exploration and development of hydrocarbon fields. We have aimed to generate a reservoir facies model for the Asmari Formation in an onshore oil field located southwest of Iran. Input data for electrofacies (EF) clustering algorithms are used, which include gamma-ray (GR), density (RHOB), porosity, and sonic logs from four wells. We obtain the petrophysical group (PG) and EF class using core data (mercury injection capillary pressure) and well-logs analysis. The integration of PGs and log EF significantly decreases the uncertainty in reservoir modeling, which alternatively enhances field development decisions. We compare the multiresolution graph-based clustering (MRGC) and k-means clustering methods. EF clustering results find nine EF classes. We delineate high-quality reservoirs based on lower GR, RHOB, and high-porosity logs. Next, we use the clustering results in the static reservoir modeling process, using the sequential index simulation and indicator kriging methods. The comparison between the facies obtained models and existing drilling core data finds that the absolute percentage error of the MRGC algorithm is less than that of the k-means algorithm. The results obtained by this study can provide useful information for the development of hydrocarbon exploration plans in the studied oil field.

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Petrophysical Modelling of Structure-Cum-Stratigraphic Play for Improved Reservoir Potential, an Integrated Field Study of L. Goru Sands, Pakistan

Khan

Abid

Fareed

et al. 2019

SPE Annual Technical Conference and Exhibition

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Technical evaluation and subsequently devising an appraisal and development strategy of a structural cum stratigraphic reservoir based on a discovery well only is always challenging. The reservoir under discussion was discovered as a structurally bounded trap and the appraisal wells were drilled on NW-SE direction along with the main bounding fault based on this understanding. However, presence of hydrocarbon below the spill point, anomalous sand thickness, lateral facies and reservoir quality variations observed in few of the wells indicated stratigraphic component in the field. Further complexity was added when the deepest tested gas was assigned on the structural map which showed extension of the hydrocarbon play outside the block boundary where the area was under different operating company that later drilled multiple wells near the block boundary. Therefore, it was critical to estimate correct initial gas in-place and percentage distribution of hydrocarbon across the lease boundaries. Figure 1 Well location map for the studied field Objective The objective of this paper is to present workflow that integrates multiple dataset to understand the field's hydrocarbon filling mechanism. Detailed geophysical and Petrophysical work has been carried out, which includes building of sequence stratigraphic framework, preparation of seismic attribute maps, understanding of the depositional setting for all the individual sand units encountered in all the wells, rock quality assessment (core and log methods with integration of capillary pressure curves), free water level (FWL) assessment, permeability modelling using machine learning approach (NN), pore throat radius estimation to relate hydrocarbon filling mechanism and saturation-height function modelling to build consistent 1D water saturation model. Datasets Integrated Comprehensive dataset has been acquired to evaluate the potential of the field that covers 3D seismic for the entire field, biostratigraphic analysis for seven (7) well, conventional logs in twelve (12) wells and advance measurements like Elemental Capture Spectroscopy and high-resolution resistivity images in five (5) wells. Core analysis data also acquired in five (5) different wells including routine core analysis, capillary pressure measurements using high pressure mercury injections, pore throat radius, relative permeability measurements (Centrifuge), formation resistivity factor measurements and sedimentological analysis (XRD & thin section) to overcome the challenges and defining the uncertainty associated with initial gas in-place. Methodology & Results Sequence based boundaries were defined to correlate individual sand bodies using the core data, image logs, elastic logs, seismic transacts and attribute maps for understanding the depositional setting. Lat-er these correlations were used to build a consistent petrophysical model including VCL estimation from Gamma/Neutron-Density/Sonic Density methods which was validated with ECS/XRD data. Porosity model was developed and validated from the core porosity followed by variable "m" estimation from the porosity/m relationship using the SCAL data. Later on, the consistent water saturation (Sw) models were built for all the studied wells. Permeability models were built using Neural Network (NN) where core-based permeability used for calibration and the model was tested qualitatively with the mobility and the well test permeability. For the validation of Sw from the logs, capillary pressure-based flow units were built using FZI/RQI, Winland & BVW (log) methods to define flow units defined through the core data. It was observed that the Winland R35 method-based pore throat radius had good correlation with the Sw log. FWL from MDT to estimate the height of the gas column, Skelt Harrison equation to capture the shape of the capillary pressure curve and Swi from the Centrifuge analysis were used to calibrate MICP end point which helped in building consistent Saturation-height functions. Results showed good to excellent match from the modeled Sw (Pc) vs Sw(log).

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Building Petrophysical Groups in a Low Permeability Carbonate Reservoir Located in Transition Zone: A Case Study from Onshore Field in UAE

Cited by 3 publications

References 2 publications

A Novel Energy Lifting Approach Using J-Function and Flow Zone Indicator for Oil Fields

A Novel Energy Lifting Approach Using J-Function and Flow Zone Indicator for Oil Fields

Integrating facies analysis and geostatistical methods in an onshore oil field using petrophysical groups

Petrophysical Modelling of Structure-Cum-Stratigraphic Play for Improved Reservoir Potential, an Integrated Field Study of L. Goru Sands, Pakistan

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