“…This shoal is high and gently sloped away into a deeper environment in all directions from the shoal ( Figure 7 ). The dominant lithotype in the lower part of the Minagish Formation is grainstone, while packstone, wackestone, and mudstone are developed in the upper part ( Figure 7 ) ( Datta et al., 2013 ). Figure 7 The depositional model of the Minagish Formation in the study area represents a homoclinal ramp extended from the inner ramp to open marine, based on the facies heterogeneity and structural effect of the area (modified from Aghaei et al., 2013 ).…”
Section: Resultsmentioning
confidence: 99%
“…There is a sharp contact which appears on the GR-log between the peloidal wackestone to packstone of the Upper Minagish Member, which is characterized by a Transgressive Systems Tract (TST), and the medium- to coarse-oolitic grainstone of the Middle Minagish member, which is characterized by a Highstand Systems Tract (HST) ( Nath et al., 2014 ; Datta et al., 2013 ) ( Figure 8 ). The Middle Oolitic grainstone is the producing reservoir deposited in a single third-order sea-level cycle and is composed of stacked broadly coarsening parasequences upward due to a high prograding ramp setting ( Nagm et al., 2018 ; Wolpert et al., 2015 ) ( Figure 8 ).…”
“…This shoal is high and gently sloped away into a deeper environment in all directions from the shoal ( Figure 7 ). The dominant lithotype in the lower part of the Minagish Formation is grainstone, while packstone, wackestone, and mudstone are developed in the upper part ( Figure 7 ) ( Datta et al., 2013 ). Figure 7 The depositional model of the Minagish Formation in the study area represents a homoclinal ramp extended from the inner ramp to open marine, based on the facies heterogeneity and structural effect of the area (modified from Aghaei et al., 2013 ).…”
Section: Resultsmentioning
confidence: 99%
“…There is a sharp contact which appears on the GR-log between the peloidal wackestone to packstone of the Upper Minagish Member, which is characterized by a Transgressive Systems Tract (TST), and the medium- to coarse-oolitic grainstone of the Middle Minagish member, which is characterized by a Highstand Systems Tract (HST) ( Nath et al., 2014 ; Datta et al., 2013 ) ( Figure 8 ). The Middle Oolitic grainstone is the producing reservoir deposited in a single third-order sea-level cycle and is composed of stacked broadly coarsening parasequences upward due to a high prograding ramp setting ( Nagm et al., 2018 ; Wolpert et al., 2015 ) ( Figure 8 ).…”
“…6 Petrophysical Groups (PG's) were determined from the MICP test samples by grouping important set of parameters using a self organinzing map or a SOM (Skalinski et al) as explained in the later sections of the paper. The 6 PG's showed conformance with already determined core Lithofacies but showed some degree of variations due to Diagnesis (Datta et al, 2013). Routine core analysis data for porosity, permeability was available over 650 plugs in 6 of the wells that included the first 3 wells having SCAL data.…”
Section: Introductionmentioning
confidence: 95%
“… Mudstone, argillaceous (1) Wackestone (2) Wackestone /Packstone (3) Pel oidal Packstones and grainstone(4) Fine to medium grained peloidooidgrainstone (5) Medium to coarse grained ooid and oncoidgrainstone/floatstone (6) Rudistboundstone (7) Algal boundstone (8) Shale (9) Two lithofacies, rudist and algal boundstones, are limited to only one well and hence it was concluded that they are very unlikely to occur extensively within Minagish field.Shale is also not used as it is limited in amount and grades to argillaceous carbonatemudstone (or marl).In summary, 6 lithofacies were identified which were prevailant in the 6 cored wells and the Lithofacies descriptions were sought to lay the foundation work of the rock typing work discussed in this paper.…”
Section: Introductionmentioning
confidence: 99%
“…A broader facies association with their depositional environment and diagenetic imprint was studied. Also an attempt was made to understand the areas of variable diagenetic effects, which led to preservation, destruction or enhancement of the original porosity (Datta et al, 2013).…”
A detailed Geological and Petrophysical characterization was achieved in a stepwise approach as part of full field 3D Reservoir Modeling and Simulation study for Minagish reservoir in the Greater Burgan field in Kuwait.Foundation of Reservoir Rock Types (RRT) was developed in first step based on Mercury Injection Capillary Pressure (MICP) dataset. A combination of Discriminant Analysis and Indexed Self Organizing Map (SOM) was used for rock type classification using hyperbolic tangent method. To improve classification of bimodal Pc curves, additional pressure values at different non-wetting phase saturations were used in conjunction with above mentioned parameters. In second step, the available Routine Core Analysis (RCA) porosity, permeability data was grouped together based on common patterns to generate rock types in RCA domain. Blind tests in two of the cored wells revealed a conformance of 81% between MICP and RCA Petrophysical Groups (PG). In the final step of the process, petrophysical groups were propagated in log domain using available log measurements common in all the wells of the field. It was challenging to establish a high level of accuracy for PG's in log domain mainly due to fine scale heterogeneity and inability of log data to capture rock fabric variation.This porosity estimate, coupled with rock type classification, helped to derive a continuous permeability log with a correlation coefficient of 0.89 validated in key cored wells. The porosity and permeability data in all the wells was incorporated in the 3D geocellular model after up-scaling honoring the unique, per rock type, Phi-K relationship.Modeled capillary pressure curves generated for each rock type in the core domain using MICP data set in 3 wells were used in saturation height modeling. The modeled equation was captured in the 3D geocellular model after populating rock types in the 3D grid to map water saturation for volumetric estimation.
A Cretaceous carbonate reservoir, deposited in a shoal complex environment, produced only 10% of the estimated STOIIP, yet currently suffering from a rapid reservoir pressure decline. Recently acquired geoscience and engineering data revealed a lot of subsurface uncertainties. To boost the production and support a pressure maintenance project, a reviewed reservoir evaluation was critical to narrow down the uncertainties on reservoir structure, Tarmat prediction, rock quality, oil distribution and connectivity of aquifer with neighboring fields.
Owing to lateral variation of depositional environments field-wise and a complex diagenetic processes history, mutli-scale heterogeneities are seen both vertically and areally. Capitalizing on limited dataset, collected from early development wells and previously overseen deep exploration wells, a fully integrated approach was required to address these heterogeneities. Fault data, integrated with log & pressure data analysis and seismically mappable Tarmat related flat spots enabled to decipher reservoir compartmentalization. Detailed property modeling used a hybrid hierarchical workflow of deterministic, statistical and stochastic techniques, and allowed capturing depositional/diagenetic rock quality variations, including seismically mappable Tarmats' overprint, calibrated with well and geochemical data.
At borehole-scale, a cascaded PCA-NNs methodology in a hierarchical order yielded the best results in predicting lithofacies at un-cored intervals using wireline logs, thus enabling more favorably the comparison with the benchmark charts than the clusters generated by directly using NN with the same original logs.
Starting from a statistically proven tight relationship between borehole lithofacies, reservoir rock types and porosity, well-calibrated inverted seismic porosity maps have been used in combination with their corresponding kriged lithofacies proportion maps, together with well/seismic based variography analysis and sedimentological/stratigraphical concepts, to generate lithofacies trend maps. Thesemaps will be the main input we used for 3D facies distribution at the field scale.
The quantification of lithofacies statistical correspondances between well and seismic inversion data, enabled to segregate between reservoir shoal facies (porous limestone), non-reservoir facies (tight limestone), and intermediate-quality facies (fine-grained packestone). Seismic-scale sedimentary/diagenetic bodies were explicitly integrated into the facies model, however high-resolution borehole facies were stochastically populated, through constraining them to pre-established lithofacies trend maps. This served to directly constrain the 3D porosity distribution and, in turn, reservoir rock types - integrating lithology, petrophysics and reservoir behavior - all closely linked to each other.
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