This study investigated the feasibility of coupling a subsurface numerical reservoir simulator (POWERS) with a surface network modeling simulator, to assist in making better field management decisions according to business need. Coupled simulation models have two advantages over uncoupled models. First, interdependence of the reservoir and surface facilities are properly modeled in coupled simulation models to accommodate rapid variations in production strategies. Coupled simulation models are likely to give more accurate production forecasts compared with modeling the reservoir or the surface separately. Second, given that most surface network modeling tools have a built-in optimizer, it is possible to allocate rates among wells based on a user's objective optimizing function, -e.g., reducing or maintaining a watercut level for a given production target -taking into consideration any system production constraints applied on a well, a group of wells or trunkline levels. To improve the quality of simulation results, a new algorithm is implemented in POWERS to calculate the inflow performance relationship (IPR), based on drainage pressure, i.e., a reservoir pressure calculated as the average of several neighboring cells in the simulation model as opposed to the single cell pressure. The current study shows that it is feasible to run coupled POWERS-surface network models and gain the benefit from the optimization algorithm of the surface network modeling tool.
It is important to employ a good production injection strategy to optimize hydrocarbon recovery from a field. Reservoir constraints on the surface facility change as the field matures. It may be economical to revise the surface facility configurations rather than retaining the initial design of the surface facility to maintain the target production level of the field as reservoir conditions change. If reservoir pressure is not sufficient to maintain natural flow, there may be need for artificial lift mechanisms to keep the well flowing. Careful analysis of interdependence of the surface facility constraints and reservoir conditions are important in designing good quality production injection strategies for these circumstances. Coupled facility and reservoir simulations allow production optimization and determination of the impact of injection or disposal policies on reservoir management. Sometimes there are oscillations in computed production rates, which may be as a result from inaccurate treatment of a coupling algorithm, well management rules, optimization techniques, etc. Excessive fluctuations make the solution impractical to implement. In this paper, we examine the cause of well rate fluctuations in coupled simulations. We were able to keep oscillation levels at a very low level in our simulations by using a small coupling interval and by revising well management rules. We made case studies with reconfiguration of surface facility designs in two large fields to examine if those designs were capable of meeting the required production targets as the reservoir conditions change with time. Surface facility models were built using a commercially available software, which were coupled to Saudi Aramco's in-house reservoir simulator, POWERS. We examine scenarios where some facilities could be eliminated to reduce cost while maintaining required production target. We study the impact of reconfiguring the surface network and examine how surface constraints might change the overall production rate.
Empirical correlations normally used to calculate pressure losses in vertical and horizontal pipes involve complex calculations that normally rely on estimation of other complex parameters such as liquid hold up and flow regimes before arriving at values of pressure losses. Errors in estimation of hold up and flow regimes using empirical correlations propagate to error in pressure loss calculation. Hence, available empirical correlations perform pressure loss calculations with certain degree of errors which can translate to poor design of production systems. Our objective is therefore to use new techniquesartificial intelligence techniques-to calculate pressure losses during natural flow of multiphase fluid through tubing in vertical wells. The method uses some real multiphase fluid flow data (such as pressure losses, flow and fluid properties) with which an artificial intelligence is developed and trained to understand the relationship between pressure losses with fluid and flow properties. The artificial intelligence is subsequently used to perform similar calculations under new fluid properties and flowing conditions. In the literature, many papers have shown that artificial neural network (ANN) technique can estimate multiphase pressure loses more accurately than some selected empirical models. However, to the best of our knowledge, none or few papers have shown the applicability of support vector machine which is another powerful AI technique. In this paper, we applied two AI techniques-artificial neural network (ANN) and support vector machine (SVM) to predict bottom-hole pressure in a multiphase flow well using field data and we then compared results with some commonly used empirical correlations commonly used in the industry using graphical and statistical error analysis. The empirical correlations used are: Duns and Ros; Hagedorn and Brown; Fancher and Brown; Mukherjee and Brill; Beggs and Brill; Orkizewski; and Petroleum experts II. Our results showed that the two AI methods predicted bottom-hole pressure with accuracies higher than the empirical models. The correlations of coefficient and results error analysis for all the models are presented in tabular and graphical forms for ease of comparison. Finally, it is worth mentioning that artificial intelligence is an emerging technology currently used in the petroleum industry to solve complex engineering problems and its application in multiphase pressure calculations is promising as shown in this paper.
Maintaining well integrity is one of the critical factors in the oil and gas industry. It requires close monitoring during the life cycle of the well, especially in offshore fields, to maximize the well life cycle and avoid catastrophic failure. Casing and bonded cement are major components of well completion that secure oil and gas production paths from different overburden formations. However, casing leaks are a common issue that might lead to serious losses in oil and gas production, locked reserves due to formation damage, personnel injuries, and severe environmental impact. Thus, it is important to detect casing leaks in the early stages to prevent such losses, which might induce a high cost of workover operations and well suspension or abandonment. Casing leaks occur due to corrosive fluids in the formations and long-term exposure to corrosive gases. During drilling, cement is set between the casing and the different formations or between the two casings for isolation and well protection. A bad cementing job leads to the failure of well barriers, cracks, and microchannels that allow corrosive fluids to migrate, which slowly corrodes casing and tubing over time. The flow direction determines the type of casing leak, either dumping (downward) or taking (upward). However, both types have a dangerous effect depending on leak severity. The identification of casing leaks, their severity, depth, and flow direction are a crucial task. Well diagnostic using the latest advanced leak detection tools is important in deciding the most appropriate remedial actions. This paper discusses a case study in a well of the Al-Khafji offshore field, where different methodologies were utilized to identify casing leaks. It involves the use of pressure/temperature profiles through downhole memory gauges, annuli pressure surveys, well-testing operations, geochemical analysis, and conventional production logs. The approach used succeeded in identifying casing leaks, flow direction, and the accurate determination of the leak location/depth.
Sustained pressure in oil and gas wells remains a serious challenge that directly impacts the well integrity. This phenomenon commonly appears in aged wells. Well completion elements provide the required isolation to protect the well. Cement prohibits fluid movement between the subsurface formations. Additionally, mechanical isolation packers are employed to alleviate the probability of tubing/annulus communication. In the Arabian Gulf, it is attributable to the presence of a shallow-depth aquifer that contains corrosive water, carbon dioxide, and hydrogen sulfide gases. Surveillance campaigns are performed to ensure the safety and healthiness of well completion components in addition to the reservoir management requirements. The Annuli Pressure Survey (APS) is deemed to be important to identify well integrity problems, especially the sustained casing/casing annulus (CCA) or tubing/casing annulus (TCA) pressure. Wells with sustain pressure are recommended for bleed-off/buildup test to check the communication between the casing sections. The annuli pressure survey campaign is run periodically in a Middle Eastern Mature Offshore Oil Field. Oil producers and water injection wells are surveyed at least twice per year. The most recent campaign for 229 wells revealed that 8.3% of the surveyed wells exhibit sustain casing pressure (SCP), and most of these wells age +20 years. This paper aims to illustrate the findings of the APS on this field. It includes the identification of the attributable factors that might lead to the SCP. It also provides a best practice to monitor the casing strings pressures and ensure well integrity in addition to diagnostic test should be followed to confirm the SCP.
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.