New wells were initially cleaned-up directly to the Plant Facilities in Tengiz. The existing facilities were not designed to process well flowback fluids which can contain contaminants, emulsions and solids from drilling and completion fluids. Well flowback operations directly to the process facilities were causing plant upsets and a backlog of wells to be placed on production. An MRM (Management of Residual Materials) spread was introduced in TCO (Tengizchevroil) in 2016 to clean-up new wells prior to performing a Plant flowback. The MRM unit utilizes an EverGreen Burner that performs fallout free and smokeless combustion of liquid hydrocarbons and residual well materials during well clean-ups. Cleaning-up new wells using the MRM spread has significantly improved the number of successful Plant flowbacks per year, lessons learned in all phases have been captured and will be shared. However, MRM clean-ups did not always guarantee a problem-free Plant flowback since only qualitative methods were being used to determine when a well had been cleaned-up sufficiently to be routed to the Plant. To improve the ability to determine a well's readiness for a Plant flowback, new quantitative methods were developed which include continuous sampling and gas-chromatography analysis along with compatibility tests. This paper will share the best practices and challenges during MRM operations, research details, field trial and results that were achieved after optimization of the flowback process.
Tengizchevroil (TCO) has several stages of put-on production (POP) process to bring the new wells online. New wells need to be cleaned up before being fully brought online and directly routed to the plants. However, reservoir conditions in Tengiz require drilling and completion methods that often results in losing significant amounts of drilling and completion fluids which comprises of barite, cuttings, and emulsions. The presence of such drilling and completion materials may and does cause plant upsets which consequently leads to significant LPO (Lost Production Opportunity). To minimize plant performance upsets and equipment problems during a controlled well ramp-up of new, reworked, and stimulated wells (or, in short: plant flowback), a prospect of data analytics application arose intending to study the leading hitters that affect the processing at the plant during flowbacks and subsequently optimize the well flowback process. By applying machine learning and statistical methodologies, a machine was given the ability to perform prognosis on plant upsets and duration of the plant flowback. The approach is broken into three main stages. Stage 1: create a consolidated history of plant flowbacks. Stage 2: determine variables that have the highest impact on plant performance (this stage would reduce the complexity of the model by removing irrelevant variables and subsequently increase the accuracy of the machine learning model). Stage 3: build an algorithm that predicts plant upsets from the variables extracted in Stage 2 (provides a data-driven method for evaluating risks associated with plant flowback). A supervised classification model was built on historic data from 2011 that evaluated plant performance risks associated with the well flowback process. Given the risk probabilities, we could predict how long it would take to complete plant flowbacks on a subset that the model had not seen before. Additionally, it was shown that given the evaluated risk for the subset the model has not seen, a sequence in which these wells were put online could have been optimized to maximize production. The feasibility of machine learning capabilities was tested on the historic plant flowback data. The study results have confirmed the intuition of the subject matter experts but in a robust and data-driven way. The model and the approach show data analytics methodologies’ applicability to optimize production operations further.
Historical events and recognition of the potential hazards caused by the adverse production environment (such as high pressure and high H2S) of existing wells in Tengiz and Korolev fields has led Tengizchevroil (TCO) to always be focused on risk mitigation and safety procedure and fully aware of the criticality of well integrity. Thus, appropriate and robust processes were developed early in the field development as a precationary step to mitigate the risk of potential well failure events. These processes later evolutioned into fully documented, automated, robust TCO Well Integrity process, which meets both industry API RP standards, Chevron Process Safety guidance and Kazakhstan’s Industrial Safety Laws. However, increasing number of TCO wells increases the possibility of potential risks considering that Well Integrity Process (WIP) requires a lot of manual work and data review by engineers. In order to improve reliability of the process, the team has developed and successfully launched the Well Integrity Portal (WIP) dashboard to automate routine duties. As a result, fully deployed WIP has provided engineers with full set of interactive dashboard functionality, which includes, but not limited to: live data/metrics visualization, automated workflows for alert/notification, non-conformance report generation, etc. This paper will provide overview of TCO’s Well Integrity program and details on how Well Integrity Portal has been developed and deployed to ensure continuous benefit to the company and incident-free operation.
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.