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.

Bringing New Levels of Automation and Flexibility to Well Testing Operations — Case Study

2021 ◽  
Author(s):  
Elias Temer ◽  
Nahomi Zerpa Mendez ◽  
Yermek Kaipov
Keyword(s):  
Data Quality ◽  
Data Acquisition ◽  
Real Time ◽  
Telemetry System ◽  
Well Testing ◽  
Well Test ◽  
Real Time Monitoring ◽  
Test Program ◽  
Wireless Telemetry ◽  
Operations Control

Abstract The oil industry has been perpetually examining well testing methods, with the goal of improving overall efficiency, ensuring data quality, and streamlining processes to achieve program objectives. Over the years, the aim of drillstem testing (DST) has remained mostly unchanged. However, operators want to meet the forecasted production investments of their fields, while improving operational efficiency and maintaining the highest level of operational standards, with safety and the environment being paramount. One of the solutions was developing a live, downhole, reservoir testing platform. The breakthrough consisted in introducing automation and real time monitoring to adjust the test program according to the actual reservoir response rather than blindly following a predefined test program, necessitating better operational flexibility. This platform is united by a wireless telemetry technology allowing an acoustic communication with downhole tools in real time. The automation of the data acquisition, downhole tools actuation and real time monitoring of the downhole operations, gives the operators the ability to perform well tests with reduced uncertainties, less human intervention and improved data quality. The early availability of reservoir knowledge enables operational efficiencies by meeting the test objectives earlier, thus reducing significantly the overall test period and the associated well testing costs. This paper describes the common well test objectives and challenges, the overall design of the wireless telemetry system, and automation of the job preparation and execution of the downhole operations that led to the successful completion of the well test campaign in very hostile condition, remote areas and restricted period. The use of the telemetry system in several well testing campaigns in different regions of the world, allowed to control critical downhole equipment and to acquire reservoir data transmittable to the clients office in town in real time. Various operation examples will be discussed to demonstrate how the automated data acquisition and downhole operations control has been used to optimize operations.

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.

A Methodology that Enables Real Time Large Volume Reservoir Testing for Deeper Reservoir Targets in Old Wells Without Damaging the Existing Upper Reservoir Layers

2021 ◽  
Author(s):  
Ozgur Karacali ◽  
Sofiane Bellabiod ◽  
Bertrand Theuveny
Keyword(s):  
Real Time ◽  
Hydraulic Pressure ◽  
Well Testing ◽  
Well Test ◽  
Time Data ◽  
Noise Sources ◽  
Drilling Rig ◽  
Network Connection ◽  
Execution Phase ◽  
Downhole Measurements

Abstract Pressure transient testing has been significantly revamped and various types have been applied for numerous motives over the past decades. In this paper, a methodology and adapted technology have been discussed in detail for enabling downhole testing operations with existing open perforations above the test packer. This methodology enabled successful downhole testing operations where conventional annulus hydraulic pressure pulse system was ruled out for numerous reasons, such as existence of perforated zones above zone of interest and/or well integrity constraints. The proposed method is based on an acoustic, wireless, bi-directional downhole to surface communication telemetry system. The process utilizes acoustic signals to control downhole tools and transmits downhole measurements in real time through a secured network connection. The procedure used in this well testing methodology is proven successful in numerous well test operations for exploration and appraisal wells in Algeria. The continuously unfolding downhole data has enabled end users and stake holders to take actions and decisions that maximized the value gain while optimizing the test durations and drilling rig utilizations. The successful application of this proposed methodology has enabled parameter estimation during the execution phase of the well testing operations. Data measured in real time is coupled with reservoir engineering interpretation to ensure meaningful sub-surface evaluation. Wellbore dynamics and several other inherent noise sources have been successfully identified to avoid snags of misinterpretation. Wells needing stimulation treatment or longer clean-up durations to enhance the well to reservoir communication quality have been handily identified in real time. The methodology has proven hydrocarbon existence in unexplored layers while enabling incorporation of additional test objectives with further assessments of zones of interest. Real time data greatly reduced uncertainties in well behavior and assisted in informed-decision-making process to adapt well test programs in real time. All well testing objectives were achieved by addressing various challenges that are inherent to conventional memory mode downhole testing operations. The methodology presented will enable the downhole testing operations through drill stem testing (DST) in complex wellbore geometries where conventional well testing approaches were rendered unattainable. The proposed solutions will warrant downhole testing of previously un-appraised formation layers that are overlain by perforated producing reservoirs. The methodology is described in detail and systematically so that the procedure and learnings from Algerian hydrocarbon producing basins can be adapted and applied to other well tests elsewhere around the globe.

scholarly journals The Importance of Implementing Cyber Physical Systems to Acquire Real-Time Data and Indicators

J ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 147-153
Author(s):  
Paula Morella ◽  
María Pilar Lambán ◽  
Jesús Antonio Royo ◽  
Juan Carlos Sánchez
Keyword(s):  
Decision Making ◽  
Data Acquisition ◽  
Real Time ◽  
Failure Detection ◽  
Cyber Physical Systems ◽  
Future Research ◽  
Time Data ◽  
Physical Systems ◽  
The Real ◽  
Real Time Data

Among the new trends in technology that have emerged through the Industry 4.0, Cyber Physical Systems (CPS) and Internet of Things (IoT) are crucial for the real-time data acquisition. This data acquisition, together with its transformation in valuable information, are indispensable for the development of real-time indicators. Moreover, real-time indicators provide companies with a competitive advantage over the competition since they enhance the calculus and speed up the decision-making and failure detection. Our research highlights the advantages of real-time data acquisition for supply chains, developing indicators that would be impossible to achieve with traditional systems, improving the accuracy of the existing ones and enhancing the real-time decision-making. Moreover, it brings out the importance of integrating technologies 4.0 in industry, in this case, CPS and IoT, and establishes the main points for a future research agenda of this topic.

A Daisy architecture for the multiprocessor real time data acquisition system of the Thor Tokamak experiment

1986 ◽  
Vol 17 (5) ◽  
pp. 285-296 ◽  
Author(s):  
Massimo Annunziata ◽  
Giuseppe Cima ◽  
Paola Mantica ◽  
Giacomo R. Sechi
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Data Acquisition System ◽  
Acquisition System ◽  
Time Data ◽  
Real Time Data
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