The "Oriente" basin is located in eastern Ecuador between the Andes Mountains and the Amazon rainforest. In 2012, daily oil production reached 505,000 barrels. The three main oil-bearing Cretaceous formations in the basin are the Hollin, T and U formations. Results from recent extensive coring of the U and Hollin formations showed that the pore size significantly affects oil saturation and production. Therefore, understanding pore size distribution can greatly enhance the success of a well. It is a major challenge to characterize and classify reservoir type and heterogeneity in reservoirs with pore-size variations using only well log data. We used core data from three wells in the U and Hollin formations to validate a new nuclear magnetic resonance (NMR) spectral analysis technique, applied in the echo domain, to estimate the pore-size distribution. In certain carbonate reservoirs in the Middle East, the distribution of pore size classes can be accurately determined by fitting the NMR pulse echoe. The method was blindly tested on three siliciclastic wells from the Oriente basin, and the results were compared with pore-size analysis from mercury-injection and capillary-pressure data. Additionally, a multi-mineral petrophysical model was built for each eall from log measurements, omitting the core data. The porosity derived from the multi-mineral model was used as a porosity input to guide the time-domain inversion of the NMR echo trains. The inversion solves for continuous logs of the porosity, attributed to three pore families, representing the range of pore-body sizes from small to medium to large. After completing the log-based classification into three pore families, the resulting porosity logs were compared to the analysis of core samples for several oilfields. For all formations and in all fields, the core-analysis inversion data was in good agreement with the time-domain NMR inversion results. These results were used to select optimum intervals to be completed and to predict production in the studied fields.
The Oriente Basin is located in eastern Ecuador at the Amazon rainforest. Shushufindi-Aguarico field is one of the most important fields in Oriente Basin with over 12% of the national production; the main hydrocarbon reservoirs are located inside the Cretaceous formations Napo and Tena. In spite of being a mature field in production since the beginning of 1970s, Shushufindi-Aguarico field still presents various formation evaluation challenges that can potentially be explored to enhance its productivity. In order to improve fluids characterization in a recently developed area at NorthWest of the field, a new reservoir evaluation technology, Fluid Logging and Analysis in Real Time, is introduced to obtain a continuous log of quantitative composition of hydrocarbon and an improving in the pay zones analysis from gas presence in the mud while drilling. The prospective intervals determination within the productive reservoirs is performed while drilling with cuttings analysis and chromatography evaluation in real time. This evaluation is based on Gas Ratio Method, which uses the relation between heavy, medium and light gases to identify porous rocks with hydrocarbon presence. The prospective intervals determination using Advanced Surface Fluid Logging technology gives more precision to identify thin beds by eliminating the recycled gas effect than conventional mud logging. In addition, the Advanced Surface Fluid Logging provides fluid composition in the C1-C5 range analogous to the PVT single phase composition. The fluid composition achieved in the main target zone exhibited a close correlation with a convention PVT from a recent offset well. This paper presents a case study where ASFL technology was tested on a Shushufindi well highlighting valuable benefits, with better pay zones definition in the challenging geological environments encountered in the Shushufindi-Aguarico field. The reliability of the data provided is demonstrated by the good correlation amongst the Fluid Logging and Analysis in Real Time composition recorded in the main target zone and a recent PVT composition from a nearby offset well.
The Auca field is located in the northern Oriente basin (Ecuador) with hydrocarbon production coming from Cretaceous fluvio-estuarine and shallow marine sandstones. The field has produced more than 547 million barrels of oil since 1972 and by the end of 2015 the field recovery factor was approximately 14%. In December 2015, the reservoir management and the field re-development activities for the Auca field were awarded to Schlumberger Production Management (SPM) under the name of Shaya project. Since then, to sustain the field re-development activities, an integrated reservoir characterization process has been implemented. In this depositional environment reservoir evaluation can be very challenging, especially when using only conventional well logs. It is proposed in this paper that the acquisition of texture dependent measurements is the solution to improve the understanding of the reservoir rocks in highly heterogeneous environments. Based on our experience in Ecuador, incorporating nuclear magnetic resonance (NMR) in the petrophysical model appears to be the best way to collect the needed texture dependent data. The Rock type characterization in the field was based on mercury injection capillary pressure data. This method enables the determination of pore throat profiles for each rock type and the dominant interconnected pore system, which corresponds to a mercury saturation of 35% in a capillary pressure curve. An empirical relationship was used to relate conventional porosity and permeability to pore throat profiles, and this was used to classify rock types. With the purpose of validating reserves and optimizing the field development plan, a model based on rock type characterization was developed using existing core, log and production data. Additionally, this model was calibrated using data from multiple fields in the basin. The propagation of the model from core to logs was accomplished through a relationship between gamma ray, density, neutron and NMR logs with core porosity and permeability in key wells. These relationships are dependent on rock type, and they were used to extrapolate core characterization to those wells without cores. Maps of rock type distribution were used to classify areas according to their petrophysical properties. These maps were also used to delineate the reservoir limits, helping to validate and identify prospective areas for future drilling and workovers. This paper presents the characterization of the reservoir into rock types by integrating geological, petrophysical and production data through Neural Network Analysis, establishing a fundamental input into and support for the development of the exploitation plan.
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