As a rule, the reservoir pressure depletion occurs with a continuous production over a period of time. This leads to high Gas-Oil Ratio and low production rates. Thus it is important to manage the pressure of reservoir and stabilize it. For this, certain IOR techniques are employed; waterflooding being one of them. This paper discusses waterflooding as a solution for a pressure management program. This work represents results of the strategic IOR program in a mature field showing importance and influence of each decision made during project implementations. The reservoir into consideration is a sandstone reservoir which was producing under a solution gas drive & a very weak aquifer for about 5 years. During the course of production, reservoir pressure decreased substantially and went below the bubble point pressure. Waterflooding was considered as the most suitable remedy to restore the reservoir pressure and well productivity, considering all the parameters. Various waterflooding plans were designed on the basis of areal & vertical sweep efficiencies and reservoir voidage compensation using material balance. The fractional flow, saturation profiles, mobility ratio and frontal advance equations were kept in mind while proposing these plans. By implementation of the most appropriate plan, the reservoir pressure was maintained 150 psi above the bubble point pressure resulting in daily production rate of about 13000 bopd with water injection rate of about 15000 bpd. The estimated oil recovery is about 52 % and breakthrough is expected to occur about 7 years after the start of waterflood. The results were not only a total reversion but also an increasing production profile that was declining over the years. Introduction This paper discusses about a case study based on the real field data, but the names of the field, different blocks in the field and wells have been changed, due to the restrictions. The Leon field is located in deltaic region of the river Azlean. It is an offshore field with water depth ranging from 20–40 m. This field was discovered in 1985 and put on production in 1993. The field is sub-divided in four blocks namely LA, LB, LC and LD. Blocks LA and LB produce high quality crude oil and they contribute about 73% of total oil production from the field. LD block is mainly a gas producer. The gravity of oil in LA and LB blocks is 35.3 and 37.2 deg API respectively with the solution GOR being 80 sm3/sm3 and 88 sm3/sm3 respectively. The oil and water viscosities are around 0.57 and 0.25 cp at reservoir conditions.
The aim of this study is to demonstrate the value of an integrated ensemble-based modeling approach for multiple reservoirs of varying complexity. Three different carbonate reservoirs are selected with varying challenges to showcase the flexibility of the approach to subsurface teams. Modeling uncertainties are included in both static and dynamic domains and valuable insights are attained in a short reservoir modeling cycle time. Integrated workflows are established with guidance from multi-disciplinary teams to incorporate recommended static and dynamic modeling processes in parallel to overcome the modeling challenges of the individual reservoirs. Challenges such as zonal communication, presence of baffles, high permeability streaks, communication from neighboring fields, water saturation modeling uncertainties, relative permeability with hysteresis, fluid contact depth shift etc. are considered when accounting for uncertainties. All the uncertainties in sedimentology, structure and dynamic reservoir parameters are set through common dialogue and collaboration between subsurface teams to ensure that modeling best practices are adhered to. Adaptive pluri-Gaussian simulation is used for facies modeling and uncertainties are propagated in the dynamic response of the geologically plausible ensembles. These equiprobable models are then history-matched simultaneously using an ensemble-based conditioning tool to match the available observed field production data within a specified tolerance; with each reservoir ranging in number of wells, number of grid cells and production history. This approach results in a significantly reduced modeling cycle time compared to the traditional approach, regardless of the inherent complexity of the reservoir, while giving better history-matched models that are honoring the geology and correlations in input data. These models are created with only enough detail level as per the modeling objectives, leaving more time to extract insights from the ensemble of models. Uncertainties in data, from various domains, are not isolated there, but rather propagated throughout, as these might have an important role in another domain, or in the total response uncertainty. Similarly, the approach encourages a collaborative effort in reservoir modeling and fosters trust between geo-scientists and engineers, ascertaining that models remain consistent across all subsurface domains. It allows for the flexibility to incorporate modeling practices fit for individual reservoirs. Moreover, analysis of the history-matched ensemble shows added insights to the reservoirs such as the location and possible extent of features like high permeability streaks and baffles that are not explicitly modeled in the process initially. Forecast strategies further run on these ensembles of equiprobable models, capture realistic uncertainties in dynamic responses which can help make informed reservoir management decisions. The integrated ensemble-based modeling approach is successfully applied on three different reservoir cases, with different levels of complexity. The fast-tracked process from model building to decision making enabled rapid insights for all domains involved.
The aim of this study is to demonstrate the value of a fully integrated ensemble-based modeling approach for an onshore field in Abu Dhabi. Model uncertainties are included in both static and dynamic domains and valuable insights are achieved in record time of nine-weeks with very promising results. Workflows are established to honor the recommended static and dynamic modeling processes suited to the complexity of the field. Realistic sedimentological, structural and dynamic reservoir parameter uncertainties are identified and propagated to obtain realistic variability in the reservoir simulator response. These integrated workflows are used to generate an ensemble of equi-probable reservoir models. All realizations in the ensemble are then history-matched simultaneously before carrying out the production predictions using the entire ensemble. Analysis of the updates made during the history-matching process demonstrates valuable insights to the reservoir such as the presence of enhanced permeability streaks. These represent a challenge in the explicit modeling process due to the complex responses on the well log profiles. However, results analysis of the history matched ensemble shows that the location of high permeability updates generated by the history matching process is consistent with geological observations of enhanced permeability streaks in cores and the sequence stratigraphic framework. Additionally, post processing of available PLT data as a blind test show trends of fluid flow along horizontal wells are well captured, increasing confidence in the geologic consistency of the ensemble of models. This modeling approach provides an ensemble of history- matched reservoir models having an excellent match for both field and individual wells’ observed field production data. Furthermore, with the recommended modeling workflows, the generated models are geologically consistent and honor inherent correlations in the input data. Forecast of this ensemble of models enables realistic uncertainties in dynamic responses to be quantified, providing insights for informed reservoir management decisions and risk mitigation. Analysis of forecasted ensemble dynamic responses help evaluating performance of existing infill targets and delineate new infill targets while understanding the associated risks under both static and dynamic uncertainty. Repeatable workflows allow incorporation of new data in a robust manner and accelerates time from model building to decision making.
Building reservoir models that consistently honors static and dynamic data is a difficult, if not impossible, task using traditional approaches resulting from limitations of existing tools and best practice workflows. The crux of this task has traditionally been to utilize dynamic data in the facies modelling process, which is often the cornerstone of the reservoir modelling workflow. Hence, failing to integrate the static and dynamic data measurements in the facies modelling process consistently can dramatically reduce the predictability of the generated reservoir models. In this paper, we efficiently solved this problem using an ensemble-based approach in combination with an adaptive pluri-Gaussian facies modelling scheme. We demonstrate the procedure on a medium size field with 15 years of production history. During the dynamic data conditioning, clear trends are established in the facies model throughout the reservoir, which provide a good indication of the expected facies distribution and associated connectivity. Having a thorough description and understanding of the subsurface uncertainties - especially when it comes to the facies model description - is key to improved reservoir management decisions when considering both optimal drainage strategies and well placement.
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