A Real-Time Automated "Smart Flow" to Prioritize, Validate, and Model Production Well Testing

2013 ◽  
Author(s):  
Alvin Stan Cullick ◽  
Miguel Villamizar ◽  
Guillermo Velasquez ◽  
Jose Antonio Rodriguez ◽  
Maiquel Manuel Querales ◽  
...  
Keyword(s):  
Real Time ◽  
Well Testing ◽  
Production Well

A Back Allocation Methodology to Estimate the Real-Time Flow and Assist Production Monitoring

2021 ◽  
Author(s):  
Gabriela Chaves ◽  
Danielle Monteiro ◽  
Virgilio José Martins Ferreira
Keyword(s):  
Real Time ◽  
Input Data ◽  
Oil Field ◽  
Well Testing ◽  
Well Test ◽  
Time Data ◽  
Time Flow ◽  
Well Model ◽  
Operational Information ◽  
The Individual

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.

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

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.

Development of an Approach for Well Testing in Real Time

2020 ◽  
Author(s):  
Yulia Airatovna Pityuk ◽  
Guzel Raifovna Akmurzina ◽  
Alfred Yadgarovich Davletbaev ◽  
Tatyana Petrovna Azarova ◽  
Dmitriy Vladimirovich Farger ◽  
...  
Keyword(s):  
Real Time ◽  
Well Testing

Fully Automated and Wirelessly Enabled Drillstem Tests: Seven-Zones Campaign Case Study in Sakhalin

2021 ◽  
Author(s):  
Elias Temer ◽  
Deiveindran Subramaniam
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Time Window ◽  
Weather Conditions ◽  
Testing Time ◽  
Well Testing ◽  
Well Test ◽  
Time Data ◽  
Operations Control ◽  
Bad Weather

Abstract Well test is one of the crucial steps required to forecast production investments of their fields. However, the operators face many challenges such as reduced capex, exploration budgets, and bad weather conditions that limit the well testing time window. To overcome these challenges, an automated well testing platform enabled a real time monitoring and controlling more zones in a single run for appraisal wells in the Sea of Okhotsk, Russia. This article highlights the test objectives, the job planning, and automated execution of wirelessly enabled operations in very hostile conditions and limited time period. The use of a telemetry system to well test seven zones allowed real-time data acquisition, control of critical downhole equipment, data transmission to the operator's office in town. Various operational cases will be discussed to demonstrate how automated data acquisition and downhole operations control has optimized operations for both the service company and the operator.

Bringing Flexibility and Automation to Well Testing Operations Through Wireless Telemetry - Case Study

2021 ◽  
Author(s):  
Elias Temer ◽  
Deiveindran Subramaniam ◽  
Yermek Kaipov ◽  
Carlos Merino ◽  
Vladimirovich Latvin ◽  
...  
Keyword(s):  
Real Time ◽  
Reservoir Characterization ◽  
Time Window ◽  
Weather Conditions ◽  
Development Planning ◽  
Testing Time ◽  
Well Testing ◽  
Well Test ◽  
Real Time Control ◽  
Field Development

Abstract Dynamic reservoir data are a key driver for operators to meet the forecasted production investments of their fields. However, many challenges during well testing, such as reduced exploration and capex budgets, complex geologic structures, and inclement weather conditions that reduce the well testing time window can prevent them from gathering critical reservoir characterization data needed to make more informed field development planning decisions. To overcome these challenges, a live, downhole reservoir testing platform enabled the most representative reservoir information in real time and connected more zones of interest in a single run for appraisal wells in the Sea of Okhotsk, Russia. This paper describes the test requirements, the prejob planning, and automated execution of wirelessly enabled operations that led to the successful completion of the well test campaign in very hostile conditions, a remote area, and restricted period. The use of a telemetry system to well testing in seven zones enabled real-time control of critical downhole equipment and acquired data at surface, which in turn was transmitted to the operator's office in town in real time. Various operation examples will be discussed to demonstrate how automated data acquisition and downhole operations control has been used to optimize operations by both the service company and the operator.

Multiphase Meter Production Well Testing Applied to Low-GOR Mature Fields

2009 ◽  
Author(s):  
Parviz Mehdizadeh ◽  
David Farchy ◽  
Jairo Suarez
Keyword(s):  
Well Testing ◽  
Production Well

Fusion of Real Time Data Transmission and Visualization to Optimize Exploration Well Testing Operations

2015 ◽  
Author(s):  
Ramzi Miyajan ◽  
Musab Khudiri ◽  
Ali Wuhaimed ◽  
Harmohan Gill ◽  
AbdulHakim Nahdi ◽  
...  
Keyword(s):  
Real Time ◽  
Data Transmission ◽  
Well Testing ◽  
Time Data ◽  
Real Time Data ◽  
Exploration Well

Maximizing Cost Efficiencies through Real-Time Well Testing

2013 ◽  
Author(s):  
Abdalla Awara ◽  
Cidar Mansilla ◽  
Kevin Fink
Keyword(s):  
Real Time ◽  
Well Testing ◽  
Cost Efficiencies
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