Identification of Patches of Bitumen in a Carbonate Reservoir: A Case Study

2021 ◽  
Author(s):  
Arit Igogo ◽  
Hani El Sahn ◽  
Sara Hasrat Khan ◽  
Yatindra Bhushan ◽  
Suhaila Humaid Al Mazrooei ◽  
...  

Abstract Carbonate reservoir X has varying levels of maturity in terms of development. The South/West is highly matured; development activities have recently kicked-off in the Crestal part while the areas towards the Far North is not fully developed and posed the largest uncertainty in terms of reservoir quality, fluid contacts, oil saturation, well injectivity/ productivity, area potential and reserves due to poor well control. In reservoir X with segmented development areas, patches of bitumen have been found in the Far North. The extent of this Bitumen was unknown. In order to expand the CO2 development concept to achieve production target from the Far Northern flank, an understanding and mitigation of the area uncertainties is crucial. Reservoir bitumen is a highly viscous, asphaltene rich hydrocarbon that affects reservoir performance. Distinguishing between producible oil and reservoir bitumen is critical for recoverable hydrocarbon volume calculations and production planning, yet the lack of resistivity and density contrast between the reservoir bitumen and light oil makes it difficult, if not impossible, to make such differentiation using only conventional logs such as neutron, density, and resistivity. This paper highlights the utilization and integration of advanced logging tools such as nuclear magnetic resonance and dielectric, in conjunction with routine logs, pressure points, RCI samples, vertical interference test and core data to differentiate between reservoir bitumen and other hydrocarbon types in the pore space. The major findings from the studies shows bitumen doesn't form as a single layer but occurs in different subzones as patches which is a challenge for static modelling. When high molecular weight hydrocarbons are distributed in the pore space and coexist with light and producible hydrocarbons, reservoir bitumen is likely to block pore throats. The Bitumen present in this reservoir have a log response similar to conventional pore fluids. The outcome of this study has helped in refining the bitumen boundary, optimize well placement, resolved the uncertainties associated with deeper fluid contacts and provided realistic estimate of STOIIP.

2007 ◽  
Author(s):  
Padmakar S. Ayyalasomayajula ◽  
Robert Edward Fitzmorris ◽  
Jairam Kamath ◽  
Mun-Hong Hui ◽  
Wayne Narr

2013 ◽  
Vol 1 (2) ◽  
pp. T143-T155 ◽  
Author(s):  
Olabode Ijasan ◽  
Carlos Torres-Verdín ◽  
William E. Preeg

Neutron and density logs are important borehole measurements for estimating reservoir capacity and inferring saturating fluids. The neutron log, measuring the hydrogen index, is commonly expressed in apparent water-filled porosity units assuming a constant matrix lithology whereby it is not always representative of actual pore fluid. By contrast, a lithology-independent porosity calculation from nuclear magnetic resonance (NMR) and/or core measurements provides reliable evaluations of reservoir capacity. In practice, not all wells include core or NMR measurements. We discovered an interpretation workflow wherein formation porosity and hydrocarbon constituents can be estimated from density and neutron logs using an interactive, variable matrix scale specifically suited for the precalculated matrix density. First, we estimated matrix components from combinations of nuclear logs (photoelectric factor, spontaneous gamma ray, neutron, and density) using Schlumberger’s nuclear parameter calculator (SNUPAR) as a matrix compositional solver while assuming freshwater-filled formations. The combined effects of grain density, volumetric concentration of shale, matrix hydrogen, and neutron lithology units define an interactive matrix scale for correction of neutron porosity. Under updated matrix conditions, the resulting neutron-density crossover can only be attributed to pore volume and saturating fluid effects. Second, porosity, connate-water saturation, and hydrocarbon density are calculated from the discrepancy between corrected neutron and density logs using SNUPAR and Archie’s water saturation equation, thereby eliminating the assumption of freshwater saturation. With matrix effects eliminated from the neutron-density overlay, gas- or light-oil-saturated formations exhibiting the characteristic gas neutron-density crossover become representative of saturating hydrocarbons. This behavior gives a clear qualitative distinction between hydrocarbon-saturated and nonviable depth zones.


10.2172/3831 ◽  
1999 ◽  
Author(s):  
R.J. Boomer ◽  
R. Cole ◽  
M. Kovar ◽  
J. Prieditis ◽  
J. Vogt ◽  
...  

2021 ◽  
Author(s):  
Saud K. Aldajani ◽  
Saud F. Alotaibi ◽  
Abdulazeez Abdulraheem

Abstract The discrimination of shale vs. non-shale layers significantly influences the quality of reservoir geological model. In this study, a novel approach was implemented to enhance the model by creating Pseudo Corrected Gamma Ray (CGR) logs using Artificial Intelligence methods to identify the thin shale beds within the reservoir. The lithology of the carbonate reservoir understudy is mostly composed of dolomite and limestone rock with minor amounts of anhydrite and thin shale layers. The identification of shale layers is challenging because of the nature of such reservoirs. The high organic content of the shales and the presence of dolomites, particularly the floatstones and rudstones, can adversely affect the log quality and interpretation and may result in inaccurate log correlations, overestimating/ underestimating Original Oil In Place (OOIP) and reservoir net pays. In such cases, Corrected Gamma Ray (CGR) curves are typically used to identify shale layers. The CGR curve response is due to the combination of thorium and potassium that is associated with the clay content. The difference between the total GR and the CGR is essentially the amount of uranium-associated organic matter. Because of the very limited number of CGR logs in this reservoir, Artificial Intelligence (AI) approach was used to identify shale volume across the entire reservoir. Synthetic CGR curves were generated for the wells lacking CGR logs using AI methods. Resistivity, Density, Neutron and total GR logs were used as inputs while CGR was set as the target. Five wells that have CGR logs were used to train the model. The created pseudo logs were then used to identify shale layers and could also be used to correct effective porosity logs. After statistical analysis of the data, two different Artificial Intelligence Techniques were tested to predict CGR logs; Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). A Sugeno-type FIS structure using subtractive clustering demonstrated the best prediction with correlation coefficient of 0.96 and mean absolute percentage error (MAPE) of 20%. The resulting synthetic CGR curves helped identify shale layers that do not extend over the entire reservoir area and ultimately correct the effective porosity logs in the reservoir model. Porosity was primarily obtained from the neutron-density logs which results in very high porosity measurements across the shale layers. This study shows a new workflow to predict shale layers in Carbonate reservoirs. The created pseudo CGR logs would help predict shale and is an added-value data that could be incorporated into the Earth model.


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