The Љdump flood completionЉ (DFC) is a mechanism conceived to inject water from a reservoir with an active aquifer into a depleted reservoir using a drilled well with marginal production or closed due to low pressure. This innovative mechanism allows for saving the costs of drilling an injector well as well as expenses associated to building additional surface facilities, and water treatment procedures.This study includes using dynamic models to evaluate scenarios linked to different DFC schemes, with the goal of increasing the net present value (NPV), while maintaining/increasing pressure in the reservoir.A multidisciplinary team developed a detailed static model to better represent within the dynamic model, the sedimentary bodies dimensions, fluids flows, and the corresponding pressure changes.The static model build used an exhaustive structural framework comprehensively coupled to the reservoir sedimentology model: facies and rock types, bodies' preferential directions, and bodies' dimensions. Before populating properties, sensitivitiess were performed to obtain an optimum grid and better represent facies lateral changes. The facies distribution model was created from the discrete curve of facies proportionality and using bodies dimensions defined in the sedimentology model. The netgross model was based on Vshale/porosity cutoffs. The porosity and NetGross characterizations were generated based on the facies modeling, and using the Sequential Gaussian Simulation (SGS) technique. The P50 of the stochastic simulations was used to establish the OOIP value. The resulting static model was input into a black oil simulator to history match production and pressure, and obtaining a base case for prediction purposes. Subsequently, the sensitiy of the reservoir performance on most significant paramters, i.e. water injection rate, bottom-hole pressure, and number and location of injector wells, was analysed. The procedure was assisted by an optimization simulation tool to obtain optimum values of net present value (NPV). The final result was considered the most optimium injection pattern applicable to the current reservoir conditions. Methodology and modeling techniques applied in this study can easily be extended to reservoirs in the basin with similar features.
The approach to the implementation of this model is supported by the integration of different disciplines as Geosciences (Geophysics, Geology, Stratigraphy, Sedimentology, Petrophysics and Geostatistics) and Reservoir Engineering; through the conformation of a multidisciplinary team with holistic vision and focusing on the Integrated Asset Management concepts in order to optimize the reservoir life cycle and its performance.The objective of this study was to evaluate by numerical simulation different scenarios in the Lower U reservoir of the Napo formation in Culebra and Yulebra fields East Basin of Ecuador. With the purpose of increasing drainage and heavy oil reserves recovery; simulation runs include drilling of directional and horizontal wells.As per heavy oil pressure maintenance and enhanced recovery; simulation includes water injection with different viscosity values (polymer simulation).As a remarkable aspect of the dynamic model generation and its evaluation through the different sub -models as components; additionally the use of an advanced simulation tool that allowed scenarios optimization helping to achieve the higher oil recoveries values with the less number of wells to be drilled.Results show the possibility of increasing the current oil recoverable reserves in 34 MMBLS by drilling 13 directional and 6 horizontal wells; besides with water injection (polymer solution) of 2 cps viscosity leads to increase the conventional scenario recovery over of 43 MM barrels of oil; it is due to the remarkable improvement in mobility ratio generating a greater sweep efficiency with an increase in oil production and a significant impact on the recovery factor (FR).In fact the total implementation of the business plan result in this study increase more than 100%, 77 MM Oil Bls (140MM compare to the official reserves of 63 MM) Also the methodology and techniques employed in this project can be used for any reservoir in the Ecuadorian basin even more too any heavy oil reservoir worldwide that have similar properties such us: thickness, depth and viscosity.
The operating plan generation of a reservoir is based on an effective dynamic characterization for recovery of reserves. To achieve this goal we must prepare, the model describes the interaction between the rock and the fluids present in the reservoir, as well as contributing to the achievement of the original reserves in place, facilitates the reproduction of the behavior of a reservoir to be undergone some enhanced recovery process. For this reason it was necessary to develop a methodology for determination of critical water saturation according to the size of Throat Poral by capillary pressure analysis performed on core samples of the fields belonging to developed areas of the Orinoco Oil Belt. In this study we propose a methodology that combined with the little information that may exist in a specific area leads to a better use of that information as a representative model of the entire stratigraphic column. These results can then be used as input for reservoir simulation models belonging to these fields, allowing the representation of heterogeneity present in the same anisotropy obtaining a representative distribution of the fluids and with this a more accurate estimation of reserves hydrocarbons and their movement within the reservoir, and generating operating plans with excellent results in the estimated potential wells. In this case the method used yielded a correlation of critical water saturation as a function of pore throat radius, with an adjustment of 90% with respect to laboratory measurements.
Rubiales is a major heavy oil field in Colombia with an OOIP larger than 5000 MSTB (Stanko, and others, 2015). The field produces from six zones mainly with horizontal wells. Production is driven by a strong aquifer which causes tilted oil-water-contact and early water breakthrough. Fully integrated reservoir modelling for field development optimization under subsurface uncertainty has been a major challenge so far. This paper presents an automated calibration process, probabilistic infill well ranking and location optimization. An automated reservoir characterization workflow was developed to generate multiple history matched models on field and well level. Static reservoir characteristics and contacts where parameterized for sensitivity assessments and calibration update steps. Variations of dynamic reservoir characteristics with an impact on model forecasting behavior were applied to alternative history matching solutions to create an ensemble of reservoir models for uncertainty assessment. Economic success criteria and a simulation opportunity index were defined for a probabilistic well ranking and optimized well location assessment. The workflow was applied to a sector of the full field including approximately 300 producer wells. Multiple history match solutions were created with 80% of the producer wells matching on well level. Quality assurance measures were applied to verify geological consistency of implemented model updates. The ensemble of forecasting models was used to deliver a probabilistic well ranking based on a well Net Present Value model. Infill well candidates with a robust performance delivery across the ensemble were identified. Results showed that a well placement scenario with half of more than 100 well candidates delivered above the economic threshold criterion and a similar recovery compared to reference field development plan. Probabilistic sweet spot maps based on a simulation opportunity index were used to efficiently identify well locations for more than 30 alternatives well candidates. The method produced robust results above the economic success criterion. Methodology and workflow design developed in this work successfully delivered a field development evaluation under subsurface uncertainty for a large heavy oil field with complex geological characteristics, long production history and large number of wells. The workflow design is applicable for other fields with similar characteristics and delivery objectives. The developing of this advanced workflow combined the application of a last-generation High-Resolution Reservoir Simulator (HRRS) and an Innovative Collaboration Environment (ICE) (Schlumberger 2020) which combines domain expertise and advanced digital technologies (ADT) enhanced quality and time results for history matching (HM) scenarios and bring the opportunity to execute several uncertainty cases for forecasting analysis allowing us to consider a wide range of results for final FDP proposed
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