Minimum miscibility pressure (MMP) is a very critical parameter to design any enhanced oil recovery affiliated with carbon dioxide (CO2) gas injection methodology. MMP can be computationally estimated using the Peng Robinson Cubic Equation of State (PR-EOS). In this paper, an association term was incorporated into the equation to account for covalent bonds between oxygen and carbon atoms in a CO2 compound for accurate MMP estimation. During the CO2 gas injection process, interactions between the oil multicomponent system and injected CO2 are in place where strong electrostatic force is exhibited between oxygen and carbon atoms. This attractive force cannot be neglected. Nevertheless, a Cubic Equation of State, such as Peng Robinson, accounts only for physical forces such as repulsion and attraction forces only. For this, an association term is introduced to account for electrostatic forces. Cubic plus Association EOS (CPA-EOS) was assimilated with Ahmed Tarek's methodology to estimate MMP rigorously in consideration of oil system and CO2 compositions. MMP was estimated using both PR-EOS and CPA-EOS, and compared against the experimental value with a very minimal absolute error. Therefore, the results showed a close agreement between calculated and experimental MMP. The uncertainty was immensely reduced when utilizing CPA-EOS proposed by Ahmed Tarek for MMP estimation. Three correlations were applied to estimate MMP with a slightly high deviation from the experimental MMP values. This high error is due to the ignorance of the intermolecular forces exhibited between molecules among these correlations. It is worth mentioning that this proposed method is highly appreciating the intermolecular bonding exhibited in CO2 and hydrocarbon multicomponent mixture, which results in a very reliable and accurate estimation of MMP. In other words, integrating conventional EOS with the association term provides accurate estimation of MMP to ensure effective modeling of an enhanced oil recovery (EOR) design with CO2 injection.
Downhole pressure and temperature sensors have been installed either separately as stand-alone sensors hanged on the production tubing of a well or jointly with Electric Submersible Pumps (ESPs) or Intelligent Well Completions (IWC). However, their utilization thus far has been limited to static/flowing bottom-hole pressures measurement for buildup/drawdown pressure tests analysis or ESP/intelligent well performance monitoring. Eighty-eight (88) wells located offshore Saudi Arabia have been equipped with ESPs combined with downhole pressure and temperature sensors installed at the intake and discharge of the pumps. Each well was equipped with a surface coriolis meter to measure the total liquid flow rate and water-cut assuming that the well's production will be maintained above the bubble point pressure. However, the coriolis meters’ readings have become erroneous ever since the wells’ flowing wellhead pressure declined to and below the saturation pressure due to the flow of liberated gas through the meters. In order to compensate for the meters’ measurement deviation, wellhead samples had to be collected and analyzed to determine the wells water-cuts where the total flow measurement was still acceptable. Alternatively, other means of multiphase flow rate measurements were used. This has proven to be costly and time consuming. This paper proposes a technique which uses real-time data transmitted from existing surface and subsurface sensors to calculate the water-cut and flow rate of each well and avoid the risky and costly field trips for wellhead sample collection and analysis. In addition, the paper describes an innovative technique to estimate the error in the measured density and calculated water-cut based on the bubble point pressure which accurately determines the application envelope of this method. The paper provides examples to illustrate the validity of the proposed technique in comparison with measured and sampled water-cuts which were collected above and below the bubble point pressure. Furthermore, the paper sheds light on the main issues impacting the method's reliability.
Smart oilfield technologies and management real-time data surveillance, in terms of reliability and availability, has proven essential in the process of prolonging asset lifespan, improving asset integrity, and proactively preventing problems. This illustrates a leadership role in the integration of cutting-edge technologies by utilizing an Intelligent Field concept. Surveillance capitalizes on real-time data transmitted from Intelligent Field equipment, where mathematical algorithms and logic are automated and imposed. The application captures specific sets of data to help identify and analyze challenges associated with Intelligent Field equipment. Major prevailing benefits include, identifying systematic techniques to utilize automated diagnostics for reduction in human intervention, develop field level surveillance, cross-validating measurements through online modeling, and further enhance collaboration. This paper details the methodology, the outcome, the requirements, and considerations associated with effective real-time data utilization in energy industry applications. The platform allows business team members and their partners to communicate, collaborate, and coordinate activities in real time.
Multiphase flow metering is considered one of the most essential technologies in well testing. Since the mid 1990's, Multiphase Flow Meters (MPFM) have evolved from a revolutionary new technology into a consolidated solution widely adopted by the major operators worldwide. This evolution has taken place thanks to the high quality of measurements, low operational cost and the capability of enabling remote monitoring of wells' performance with ease. The meters have proven to be durable for testing oil wells of Saudi Aramco's (SA) fields. The installed MPFMs in Northern fields of Saudi Aramco have contributed effectively to enhancing the semi-real time measurements' quality as well as improving reservoir characterization. However, MPFMs are prone to mechanical and firmware failures. To keep the general health of the installed systems, various preventive and ad-hoc services are needed. The MPFM technology largely seems to be performing as expected and delivering high quality data. So far, 58% of the systems have been in service for more than 8 years and continuing to operate efficiently. Thorough analysis of the routine maintenance and checkups conducted during the past 10 years revealed the following: The MPFMs Mean Time between Failure (MTBF) and Mean Time to Repair (MTTR) indicated that the MPFMs have been available 97% of the time.The use of a probabilistic approach (Monte-Carlo™ Simulation) to study the P10, P50 and P90 values of the maintained MPFMs indicated that the variance between these values is small, demonstrating the reliability of these equipment.The classification of the diverse types of problems faced while operating the MPFMs highlighted the dominant cause of failures and enabled the initiation of a surveillance and subsequent failure mitigation program. The latter program resulted in fewer failures and higher equipment efficiency. This paper discusses the lessons learnt and experience gained from operating 168 MPFMs provided by different vendors in different environments during a 10-year period, along with the appropriate solutions that were implemented to mitigate the challenges faced and subsequently improve the efficiency and data quality of the MPFM.
The well drainage pressure and radius are key parameters of real-time well and reservoir performance optimization, well test design and new wells' location identification. Currently, the primary method of estimating the well drainage radius is buildup tests and their subsequent well test analysis. Such buildup tests are conducted using wireline-run quartz gauges for an extended well shut-in period resulting in deferred production and risky operations.A calculation method for predicting well/reservoir drainage pressure and radius is proposed based on single-downhole pressure gauge, flowing well parameters and PVT data. The proposed method uses a simple approach and applies established well testing equations on the flowing pressure and rates of a well to estimate its drainage parameters. This method of estimation is therefore not only desirable, but also necessary to eliminate shutting-in producing wells for extended periods; in addition to avoiding the cost and risk associated with the wireline operations. The results of this calculation method has been confirmed against measured downhole, shut-in pressure using wireline run gauges as well as dual gauge completed wells in addition to estimated well parameters from buildup tests. This paper covers the procedure of the real-time estimation of the well/reservoir drainage pressure and radius in addition to an error estimation method between the measured and calculated parameters. Furthermore, the paper shows the value, applicability and validity of this technique through multiple examples.
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.