World First All-Electric Intelligent Completion System with Permanent Monitoring to Evaluate Injection Performance in a Mature Injector Well

Abstract Traditional waterflooding methods in heavy oil fields can lead to several problems including reductions of swapping efficiency, channeling of injected water, and low values of recovery factor. These problems are often made worse by other critical factors such as lack of real-time data, operational incidents during injection profile calibration, and complexity of interventions in the existing wells. An innovative solution was implemented in a four-zone injector well in a heavy oil field in Colombia consisting of four intelligent electric valves controlled remotely and distributed fiber optic monitoring to calculate injected flow per zone in real-time. This system allowed the operator to increase oil production in the associated producer wells and eliminate rig-less interventions. The first installation of an All-Electric intelligent completion with distributed fiber optic monitoring was successfully deployed in a complex existing injector well without HSE incidents nor deviations in time and cost. After one year of operation, the system increased production in corresponding producer wells by 62% and saved 30% of operational costs. Additionally, the completion design has improved the injection performance which means that the system requires less injected water to produce the same amount of oil. All these results were possible thanks to the use of a more efficient injection completion and the use of real-time data to make on-time decisions. The importance of this implementation is that it demonstrated that this type of technology not only solves different challenges of the Enhanced Oil Recovery (EOR) strategies of mature fields but also brings additional value in terms of oil production, injection performance, and reduction in operational costs. In this way, this application showed that an intelligent completion - usually expensive in terms of initial investment - is financially viable to implement in mature existing wells with limited CAPEX availability. This paper will present the implementation of an intelligent well completion system that uses permanent distributed fiber optics to monitor water injection in 4 independent zones. The document will also include details regarding the reasons to install this technology in a mature field, well and technology selection, intelligent completion design, and installation. Results will be compared to conventional completion for injector wells that depends on rig-less intervention to measure and regulate injected flow per zone.

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A Novel Well Test Data Analyzer and Process Optimizer Using Artificial Intelligence and Machine Learning Techniques

10.2118/206137-ms ◽

2021 ◽

Author(s):

Nagaraju Reddicharla ◽

Subba Ramarao Rachapudi ◽

Indra Utama ◽

Furqan Ahmed Khan ◽

Prabhker Reddy Vanam ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Real Time ◽

Test Data ◽

Oil Field ◽

Well Testing ◽

Well Test ◽

Time Data ◽

Real Time Data ◽

Well Tests

Abstract Well testing is one of the vital process as part of reservoir performance monitoring. As field matures with increase in number of well stock, testing becomes tedious job in terms of resources (MPFM and test separators) and this affect the production quota delivery. In addition, the test data validation and approval follow a business process that needs up to 10 days before to accept or reject the well tests. The volume of well tests conducted were almost 10,000 and out of them around 10 To 15 % of tests were rejected statistically per year. The objective of the paper is to develop a methodology to reduce well test rejections and timely raising the flag for operator intervention to recommence the well test. This case study was applied in a mature field, which is producing for 40 years that has good volume of historical well test data is available. This paper discusses the development of a data driven Well test data analyzer and Optimizer supported by artificial intelligence (AI) for wells being tested using MPFM in two staged approach. The motivating idea is to ingest historical, real-time data, well model performance curve and prescribe the quality of the well test data to provide flag to operator on real time. The ML prediction results helps testing operations and can reduce the test acceptance turnaround timing drastically from 10 days to hours. In Second layer, an unsupervised model with historical data is helping to identify the parameters that affecting for rejection of the well test example duration of testing, choke size, GOR etc. The outcome from the modeling will be incorporated in updating the well test procedure and testing Philosophy. This approach is being under evaluation stage in one of the asset in ADNOC Onshore. The results are expected to be reducing the well test rejection by at least 5 % that further optimize the resources required and improve the back allocation process. Furthermore, real time flagging of the test Quality will help in reduction of validation cycle from 10 days hours to improve the well testing cycle process. This methodology improves integrated reservoir management compliance of well testing requirements in asset where resources are limited. This methodology is envisioned to be integrated with full field digital oil field Implementation. This is a novel approach to apply machine learning and artificial intelligence application to well testing. It maximizes the utilization of real-time data for creating advisory system that improve test data quality monitoring and timely decision-making to reduce the well test rejection.

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