• Economic and Environmental Geology
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• v.44 no.1
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• pp.83-94
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• 2011

Extra heavy oil reservoirs are distributed over the world but most of them is deposited in the northern part of the Orinoco River in Venezuela, in the area of 5,500 $km^2$, This region, which has been commonly called "the Orinoco Oil Belt", contains estimated 1.3 trillion barrels of original oil-in-place and 250 billion barrels of established reserves. The Venezuela extra heavy oil has an API gravity of less than 10 degree and in situ viscosity of 5,000 cP at reservoir condition. Although the presence of extra heavy oil in the Orinoco Oil Belt has been initially reported in the 1930's, the commercial development using in situ cold production started in the 1990's. The Orinoco heavy oil deposits are clustered into 4 development areas, Boyaco, Junin, Ayachoco, and Carabobo respectively, and they are subdivided into totally 31 production blocks. Nowadays, PDVSA (Petr$\'{o}$leos de Venzuela, S.A.) makes a development of each production block with the international oil companies from more than 20 countries forming a international joint-venture company. The Eastern Venezuela Basin, the Orinoco Oil Belt is included in, is one of the major oil-bearing sedimentary basins in Venezuela and is first formed as a passive margin basin by the Jurassic tectonic plate motion. The major source rock of heavy oil is the late Cretaceous calcareous shale in the central Eastern Venezuela Basin. Hydrocarbon materials migrated an average of 150 km up dip to the southern margin of the basin. During the migration, lighter fractions in the hydrocarbon were removed by biodegradation and the oil changed into heavy and/or extra heavy oil. Miocene Oficina Formation, the main extra heavy oil reservoir, is the unconsolidated sand and shale alternation formed in fluvial-estuarine environment and also has irregularly a large number of the Cenozoic faults induced by basin subsidence and tectonics. Because Oficina Formation has not only complex lithology distribution but also irregular geology structure, geological evolution and characteristics of the reservoirs have to be determined for economical production well design and effective oil recovery. This study introduces geological formation and evolution of the Venezuela extra heavy oil reservoirs and suggest their significant geological characteristics which are (1) thickness and geometry of reservoir pay sands, (2) continuity and thickness of mud beds, (3) geometry of faults, (4) depth and geothermal character of reservoir, (5) in-situ stress field of reservoir, and (6) chemical composition of extra heavy oil. Newly developed exploration techniques, such as 3-D seismic survey and LWD (logging while drilling), can be expected as powerful methods to recognize the geological reservoir characteristics in the Orinoco Oil Belt.

• Applied Chemistry for Engineering
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• v.24 no.1
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• pp.10-17
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• 2013

Heavy oil residue upgrading process was being used in conventional refinery process. Recently, as the importance of non conventional energy development is growing up, the commercial projects of heavy oil upgrading are getting more active than before. For having competitive business model in the resource competition, non conventional energy development should be considered as an important business strategy. In developing oil sands, extra heavy oil, and shale gas, canadian oil sands and extra heavy oil have great importance in substitution of conventional oil consumption. In oil sands development, the bitumen, which is extracted from oil sands, has great value after upgrading or refining process. Similar process is being used current conventional refinery process. The bitumen is highly viscous hydrocarbon. This bitumen includes impurities which can not be treated in conventional refinery process. As this reason, specified process is needed in bitumen or extra heavy oil upgrading process. Moreover, there will be additional specified facilities in the process of production, transportation and marketing. In oil sands, there are various kinds of commercial upgrading process. Extraction, dilution, coking and cracking method were being used commercially.

• Applied Chemistry for Engineering
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• v.25 no.1
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• pp.113-115
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• 2014

Lab scale simulated steam assisted gravity drainage (SAGD) process devices were used to investigate the effect of inorganic additives for the bitumen recovery from oil sand. An extra heavy oil similar with bitumen and 1.5 mm diameter of the glass bead instead of clay was mixed to simulate the oil sand. In addition, $FeO_X$ synthesized from the inorganic process was introduced as an inorganic additive for improving the recovery. Finally, the steam heat transfer rate of approximately 40% following the introduction of inorganic additives which also increased the recovery rate by about 30%.

• Applied Chemistry for Engineering
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• v.25 no.3
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• pp.262-267
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• 2014

SAGD (steam assisted gravity drainage) process is the most commonly used in-situ technology for the recovery of bitumen from oil sand. It was investigated that the effects of different additives on bitumen recovery rate from oil sand in SAGD process among many possible mechanisms studied throughout the study. Bitumen recovery from thin layer oil sand reservoirs was simulated by using an experimental SAGD apparatus with scale of 150:1. To improve the simulation accuracy of thin layer oil reservoir, we have attached geological model (GM). Oil sand was simulated by using a mixture of extra heavy oil and glass beads with a diameter of 1.5 mm. $CO_2$ was used as an additive and the evolution of steam chambers were closely monitored, and the effects of $CO_2$ as an additive was investigated. Two types of injection methods were tested; continuous ($cCO_2$-SAGD) and sequential interruption ($sCO_2$-SAGD) $CO_2$ injection. For the $sCO_2$-SAGD experiment, it was observed that the recovery rates and CSOR were efficiently improved control experiment from 60.2% to 69.3% and 7.1 to 6.0, respectively, whereas $cCO_2$-SAGD experiment decreased from 60.2% to 57.6% and 7.1 to 7.3.

• Geophysics and Geophysical Exploration
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• v.13 no.1
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• pp.80-87
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• 2010

This study was conducted to develop a reservoir modelling workflow to reproduce the heterogeneous distribution of effective permeability that impacts on the performance of SAGD (Steam Assisted Gravity Drainage), the in-situ bitumen recovery technique in the Athabasca Oil Sands. Lithologic facies distribution is the main cause of the heterogeneity in bitumen reservoirs in the study area. The target formation consists of sand with mudstone facies in a fluvial-to-estuary channel system, where the mudstone interrupts fluid flow and reduces effective permeability. In this study, the lithologic facies is classified into three classes having different characteristics of effective permeability, depending on the shapes of mudstones. The reservoir modelling workflow of this study consists of two main modules; facies modelling and permeability modelling. The facies modelling provides an identification of the three lithologic facies, using a stochastic approach, which mainly control the effective permeability. The permeability modelling populates mudstone volume fraction first, then transforms it into effective permeability. A series of flow simulations applied to mini-models of the lithologic facies obtains the transformation functions of the mudstone volume fraction into the effective permeability. Seismic data contribute to the facies modelling via providing prior probability of facies, which is incorporated in the facies models by geostatistical techniques. In particular, this study employs a probabilistic neural network utilising multiple seismic attributes in facies prediction that improves the prior probability of facies. The result of using the improved prior probability in facies modelling is compared to the conventional method using a single seismic attribute to demonstrate the improvement in the facies discrimination. Using P-wave velocity in combination with density in the multiple seismic attributes is the essence of the improved facies discrimination. This paper also discusses sand matrix porosity that makes P-wave velocity differ between the different facies in the study area, where the sand matrix porosity is uniquely evaluated using log-derived porosity, P-wave velocity and photographically-predicted mudstone volume.