Carbon dioxide flooding is considered one of the most commonly used miscible gas injection to improve oil recovery and its applicability has grown significantly due to its availability, greenhouse effect and easy achievement of miscibility relative to other gasses. Therefore, miscible CO2-injection is considered one of the most feasible methods worldwide. For long term strategies in Iraq and the Middle East, most oil fields will need to improve oil recovery as oil reserves are falling. This paper presents a study of the effect of various miscible CO2-injection scenarios on the performance of the highly heterogeneous clastic reservoir in Iraq. An integrated field-scale reservoir simulation model of miscible CO2-flooding is accomplished. The compositional simulator, Eclipse-300 has been used to investigate the feasibility of miscible CO2-injection process. The process of the continuous CO2-injection was optimized to start in January 2056 as an improved oil recovery method after natural depletion and water flooding processes have been performed, and it will continue until January 2063. The minimum miscibility pressure (MMP) for CO2 was determined using empirical correlation as a function of crude oil composition and its properties. Ten miscible CO2-injection options were undertaken to investigate the reservoir performance. These options included applying a wide range of the CO2-injection rates ranged between 1.25 to 50 MMScf/day. All development options were analyzed with respect to net present value (NPV) calculations to confirm the more feasible CO2development strategy. The results showed that the application of CO2-injection option of a 20 MScf/day attained the highest recovery of 28% by January 2063 among the others. The recovery growth was so minor by the increasing the CO2-injection rate above this level. Based on economic findings the option of 20 MScf/day also attained the highest net present value (NPV). The results showed that after January 2063, the oil recovery attained by the different CO2-injection options are less than the one attained by the waterflooding process. Therefore, the miscible CO2-injection became unviable economically after January 2063.
The developing of an immature brown oilfield through determining the optimal number and locations of infill wells pose extreme challenges due to high costs of drilling wells and uncertainty of geological and reservoir characteristic parameters. The improper well placement may lead to project failure. This paper presents a methodology to identify reservoir layers with high potential opportunities for optimal infill well placement in Nahr Umr reservoir of Subba oilfield. A full field numerical flow simulation model was constructed to assist in simulation the opportunity index and generate opportunity index (OI) which has been determined depending on static and dynamic properties. The generated OI maps for each layer in the reservoir assist in delineating the reservoir regions with favorable infill wells placement. This methodology was efficient, easy to apply and less time consuming as well as it reduce the uncertainty inherent to infill well placement. The presence of water aquifer drive and needing for waterflooding after few years of field life to maintain the field pressure, make the infill drilling with economic viability very crucial element in the field development plan. The performance of new wells, tested at different places of the field in the simulation model, had produced high watercut. It is very important to determine the reservoir opportunities zones in order to enhance sweeping efficiency and increase oil production rate from infill well. In this study a combination of extracted parameters from both static and dynamic models were used to generate opportunity index maps through writing codes into classic property calculator in the Eclipse-FloViZ commercial simulator software. In this case study of Nahr Umr reservoir in Subba oilfield the results showed distinct identification reservoir potential regions for each layer at the reservoir at last time step of history match. These regions showed high OI values that reflected the actually mapped oil in each layer at that time.
Different from the imposed linear-relationship in multiple linear regressions, Multivariate Adaptive Regression Spines (MARS) is a nonparametric regression procedure that automatically fits the relationship between variables taking into account non-linearity. In this paper, MARS was adopted for multiscale construction of a relationship between core permeability given the Computer-Processed Indicators (CPI) of multiple well log records in a sandstone reservoir.In MARS, a set of coefficients and basis functions, which are driven for the regression data, are used to construct the relationship between response variable (core permeability) and predictors (well log data and lithofacies). Different from other techniques, MARS is suitable for high dimensional predictors (multiple predictors) because the basis functions partition the input data into regions, each with its own coefficients set. Additionally, MARS has the ability of overcoming the possible outliers that might be available in the given data set. Likewise, MARS automatically eliminates the predictors that have no influence on the response.The Computer-Processed Indicators (CPI) encompass: water saturation, shale volume, and neutron porosity. Lithofacies of sand, shaly sand, and shale were also included, in the modeling, to provide three different models given these discrete lithofacies classes. Consequently, MARS algorithm has proven its efficiency to model the distinct scales and construct the non-linear relationship between core permeability and CPI log data, given the lithofacies, by providing accurate coefficient estimation and prediction.The MARS results were compared with the common approach of Generalized Linear Model (GLM). MARS led to much more accurate modeling and prediction than GLM as the coefficient of multiple determination was much higher and the root mean square prediction error was much less than the GLM. Accuracy of MARS algorithm came from taking into account the non-linearity between data into modeling; GLM consider simple linear relationship to fit the distinct scales data.
This paper presents an optimization analysis of different development options of Nahr Umr reservoir in Subba oilfield. Nahr Umr reservoir is a highly heterogeneous clastic reservoir with moderate edge a bottom waterdrive and it has a short production history for six months. Many different development scenarios have been conducted in this study to test possible predictive scenarios to determine the most significant development option. The black oil commercial software simulator, Eclipse-100 was used to study fluid flow in the reservoir and to predict the future behavior of reservoir. The prediction scenarios considered in this study include, natural depletion through the existing wells, determine the optimal number of infill producers, and waterflooding option through conducting peripheral and five-spot patterns. The development plan assumed to commence at January 2017, then couple of runs using simulator flow model were conducted for ten years. The waterflood sceneries startedup on January 2023 and January 2027 for inverted five-spot and peripheral patterns respectively with 58 producers and 66 injectors for peripheral pattern and 102 producers and 77 injectors for inverted five-spot pattern. The results are analyzed based on economic criteria to optimize the number of infill drilling. The optimization of development options was achieved based on net present values analysis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.