An Environmental Assessment of Proposed Geothermal Well Testing in the Tigre Lagoon Oil Field, Vermilion Parish, Louisiana

Abstract Commingle production nodes are standard practice in the industry to combine multiple segments into one. This practice is adopted at the subsurface or surface to reduce costs, elements (e.g. pipes), and space. However, it leads to one problem: determine the rates of the single elements. This problem is recurrently solved in the platform scenario using the back allocation approach, where the total platform flowrate is used to obtain the individual wells’ flowrates. The wells’ flowrates are crucial to monitor, manage and make operational decisions in order to optimize field production. This work combined outflow (well and flowline) simulation, reservoir inflow, algorithms, and an optimization problem to calculate the wells’ flowrates and give a status about the current well state. Wells stated as unsuited indicates either the input data, the well model, or the well is behaving not as expected. The well status is valuable operational information that can be interpreted, for instance, to indicate the need for a new well testing, or as reliability rate for simulations run. The well flowrates are calculated considering three scenarios the probable, minimum and maximum. Real-time data is used as input data and production well test is used to tune and update well model and parameters routinely. The methodology was applied using a representative offshore oil field with 14 producing wells for two-years production time. The back allocation methodology showed robustness in all cases, labeling the wells properly, calculating the flowrates, and honoring the platform flowrate.

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Unlocking Idle Production in an Offshore Sandstone Oil Field With Gas Hydrate Issues

10.2118/200904-ms ◽

2021 ◽

Author(s):

Babalola Daramola

Keyword(s):

Gas Hydrate ◽

Oil Production ◽

Oil Field ◽

Third Party ◽

Flow Assurance ◽

Well Testing ◽

Production Wells ◽

Oil Flow ◽

Offshore Oil ◽

Production Losses

Abstract This publication presents how an oil asset unlocked idle production after numerous production upsets and a gas hydrate blockage. It also uses economics to justify facilities enhancement projects for flow assurance. Field F is an offshore oil field with eight subsea wells tied back to a third party FPSO vessel. Field F was shut down for turnaround maintenance in 2015. After the field was brought back online, one of the production wells (F5) failed to flow. An evaluation of the reservoir, well, and facilities data suggested that there was a gas hydrate blockage in the subsea pipeline between the well head and the FPSO vessel. A subsea intervention vessel was then hired to execute a pipeline clean-out operation, which removed the gas hydrate, and restored F5 well oil production. To minimise oil production losses due to flow assurance issues, the asset team evaluated the viability of installing a test pipeline and a second methanol umbilical as facilities enhancement projects. The pipeline clean-out operation delivered 5400 barrels of oil per day production to the asset. The feasibility study suggested that installing a second methanol umbilical and a test pipeline are economically attractive. It is recommended that the new methanol umbilical is installed to guarantee oil flow from F5 and future infill production wells. The test pipeline can be used to clean up new wells, to induce low pressure wells, and for well testing, well sampling, water salinity evaluation, tracer evaluation, and production optimisation. This paper presents production upset diagnosis and remediation steps actioned in a producing oil field, and aids the justification of methanol umbilical capacity upgrade and test pipeline installations as facilities enhancement projects. It also indicates that gas hydrate blockage can be prevented by providing adequate methanol umbilical capacity for timely dosing of oil production wells.

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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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Well-testing in travale-radicondoli field. Part I. Geothermal well test analysis, principles and practice

Geothermics ◽

10.1016/0375-6505(78)90005-6 ◽

1978 ◽

Vol 7 (2-4) ◽

pp. 145-150 ◽

Cited By ~ 7

Author(s):

P. Atkinson ◽

A. Barelli ◽

W. Brigham ◽

R. Celati ◽

G. Manetti ◽

...

Keyword(s):

Well Testing ◽

Well Test ◽

Well Test Analysis ◽

Test Analysis ◽

Geothermal Well

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Holistic Environmental Assessment and Offshore Oil Field Exploration and Production

Marine Pollution Bulletin ◽

10.1016/s0025-326x(00)00115-6 ◽

2001 ◽

Vol 42 (1) ◽

pp. 45-58 ◽

Cited By ~ 9

Author(s):

Edward Salter ◽

John Ford

Keyword(s):

Environmental Assessment ◽

Oil Field ◽

Exploration And Production ◽

Offshore Oil

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Oil Well Testing Using Production Logging Tool in Khurmala field in Kurdistan Region-Iraq

UKH Journal of Science and Engineering ◽

10.25079/ukhjse.v3n2y2019.pp41-51 ◽

2019 ◽

Vol 3 (2) ◽

pp. 41-51

Author(s):

Maha Hamoudi ◽

Akram Humoodi ◽

Bashdar Mohammed

Keyword(s):

Saturation Pressure ◽

Water Entry ◽

Oil Wells ◽

Oil Field ◽

Oil Well ◽

Well Testing ◽

Kurdistan Region ◽

Injection Wells ◽

Study Results ◽

Entry Points

Production logging tools (PLTs) in oil and gas industries are used for obtaining fluid types and measuring fluid rates in the borehole for both production and injection wells and to better understand the well productivity or the well injectivity of the interest zones. Additionally, it can be used to detect well problems, such as early water or gas breakthrough, channeling behind casing or tubing, and water or gas coning. The Khurmala field is a big oil field in the Kurdistan region of Iraq. PLTs have been acquired in many of the Khurmala oil wells, and the log records took into consideration the production technique decisions. In this study, results of the PLT log will be discussed in one of the Khurmala oil wells. Owing to the long history of production of oil or gas wells, many problems have been seen, such as coning either water or gas, formation damage, casing corrosion, and well obstruct. This research will evaluate the production profile across the slotted liner interval of (W1) well in the Khurmala oil field in the Iraq-Kurdistan region and detect possible water entry points, verify the distribution and nature of fluids, and estimate fluid segregation after the shut-in period. This was done by applying PLTs and interpreting the data by using Emeraude software. The performance of each choke size was studied and assessed. It was found that a choke size of 48/64̎gives the best favorable production gas, oil ratio, and profile. Results from the PL survey showed that no water entry was detected across the logged interval. All the water was coming from below a depth of 990 m; most of the hydrocarbons were coming from the slotted interval across 981.8-982.9 m, and the flowing pressure across the logged interval using maximum choke was less than the saturation pressure.

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