The Shushufindi field is located in the Oriente basin of Ecuador. The field was discovered in 1972 and is sparsely developed with about 200 wells covering an area of approximately 400 km2. An initial lithofacies characterization based on capillary pressures, pore throat size distribution, and log derived total porosities and permeabilities has provided a coherent grouping for the reservoir types that matched in over 92% of the wells. The importance of pore throat size in the initial lithofacies characterization has spurred the use of nuclear magnetic resonance (NMR) logs. NMR laboratory measurements on core samples complement the characterization with continuously calibrated pore body size derivations. Furthermore extensive whole core studies have shown that the pore throat size has a substantial impact on the initial oil saturation. Failures to respect this later point had previously generated disappointing oil production results. The challenge for the subsurface and engineering team is to take advantage of a lithofacies-based method to determine appropriate completion intervals and generate reliable production predictions. Open hole wireline log measurements of derived pore bodies size show a good correlation to NMR laboratory measurements on plugs when grouped within the initial lithofacies characterization. These data are used to provide a calibrated continuous pore body size curve based on the NMR binned porosities, and using the inferred initial oil saturation, to estimate the flow capacities for each lithofacies. This approach provides adequate support for selecting the reservoir rock with the best flow capacity, thereby optimizing the well completion. In thin beds where the vertical resolution of standard density and NMR logs is insufficient to discriminate the best facies a modified method has been developed. Very high resolution imaging has been coupled with flushed zone (Rxo) measurements and high frequency dielectric dispersion log to identify the potential production. This paper presents a lithofacies characterization enhancement methodology using NMR derived pore body size as implemented in the Shushufindi field, the results to date, its impact on the selection of completion intervals, and the resulting improvement in production from this technique.
The Shushufindi field is located in the Oriente basin of Ecuador. The field was discovered in 1972 and widely developed with about 247 wells covering an area of approximately 400 km 2 . The implementation of lithofacies characterization in 98% of the existing wells has given a reliable description in about 92% of the wells in the current geomodel, which demonstrates, the validity of the deterministic method.A robust petrophysical rock type (PRT) classification can significantly improve the chances of success for all wells, focusing on layered reservoir rocks recognized as the major energy resource in recent years. The vertical and lateral classification of rock heterogeneity in the form of rock types is critical to understand the flow dynamics of the reservoirs. Well logs are the best option for formation evaluation as they provide high vertical resolution measurements. However, rock type's classification using only well logs interpretation techniques, has its limits.In this paper, we introduce a rock type neural network technique based on Indexed and Probabilistic Self-Organized Mapping (IPSOM) which was designed for the geological interpretation of well log data, facies prediction and optimal derivation of petrophysical parameters. The rock typing was based on cored wells in a 3-step approach. Preliminary rock type identification was based on sedimentology description and routine core analysis. In parallel, it was refined with high pressure mercury injection data to describe accurately the porous media. The porosity and permeability ranges were established to elaborate a sand facies classification represented by Petrophysical Rock Type through Winland method. The neural network was first trained on cored reservoirs, and then propagated to uncored wells using the classification model relationship with electrical logs. Finally using the IPSOM classification model, a permeabilityporosity relationship for each rock type was obtained, providing input to the dynamic model to predict and validate permeability. This paper present a reservoir characterization enhancement technique using neural network, which has proven its utility in refining the dynamic model of the Shushufindi field and directly contributing to the operator by improving production from layered reservoirs.
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 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.
The Shushufindi field, located in the Oriente basin of Ecuador, has been producing since 1972. In December 2011, the field had approximately 150 wells, with a total production of 45,000 bopd. Field operations were then passed to the Consorcio Shushufindi (CSSFD) led by Schlumberger for a period of 15 years. Since then, 30 new wells and 26 workovers have been completed by Schlumberger Production Management (SPM) in the field, with a production of 60,000 bopd in May 2013.A methodology with four successive distinct phases was created, aiming for a complete reservoir characterization over 18 months prior to delivering the five-year field development plan (FDP) in October 2013. Each petrophysical phase focused on providing a reliable basis for the first two years of operations, and regular updates to the static and dynamic models.In conjunction with operations support, a comprehensive data acquisition plan was launched with advanced core analysis and special logs to support the advanced reservoir characterization. The deployment of technologies such as the Dielectric Scanner* multifrequency dielectric dispersion service, the combinable magnetic resonance tool, and the FMI* fullbore formation microimager, among others, was key in revealing the reservoir's true characteristics.Advanced petrophysics delivered a description of the main reservoir heterogeneities and properties, along with a facies characterization tied to an advanced core analysis including capillary pressure and pore throat size measurements. From this, hydraulic flow units were established in a deterministic characterization over the waterflood pilot well patterns. Additionally, a quantitative evaluation of thin beds and low resistivity pay zones provides the potential for their incorporation in the original oilin-place computation. These findings have contributed to a revision of the depositional concepts for the field.
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