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.

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.

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.

Alternate mode for data acquisition and real-time monitoring system based on CAMAC system

2006 ◽  
Vol 81 (15-17) ◽  
pp. 1771-1774 ◽  
Author(s):  
J.R. Luo ◽  
P.J. Wei ◽  
G.M. Li ◽  
H. Wang
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Monitoring System ◽  
Real Time Monitoring

Alternate Data Acquisition and Real-time Monitoring System on HT-7 Tokamak

2005 ◽  
Vol 7 (6) ◽  
pp. 3114-3116 ◽  
Author(s):  
Wei Peijie ◽  
Luo Jiarong ◽  
Wang Hua ◽  
Li Guiming
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Monitoring System ◽  
Real Time Monitoring

Reservoir Test Optimization in Real Time with New Wireless Telemetry System

2010 ◽  
Author(s):  
Abdulhadi Hakim Al-Nahdi ◽  
Tarek Said Abo Elsoud ◽  
Erwann Lemenager ◽  
Matthew James Loth ◽  
Foued Mabrouki ◽  
...  
Keyword(s):  
Real Time ◽  
Telemetry System ◽  
Wireless Telemetry ◽  
Test Optimization

scholarly journals ZigBee-Based Telemetry System

2010 ◽  
Vol 7 (2) ◽  
pp. 32 ◽  
Author(s):  
L. Khriji ◽  
F. Touati ◽  
N. Hamza
Keyword(s):  
Real Time ◽  
Mobile Device ◽  
Low Cost ◽  
Base Station ◽  
The Body ◽  
Telemetry System ◽  
Real Time Monitoring ◽  
Digital Convergence ◽  
Machine Interface

 Nowadays, there is a significant improvement in technology regarding healthcare. Real-time monitoring systems improve the quality of life of patients as well as the performance of hospitals and healthcare centers. In this paper, we present an implementation of a designed framework of a telemetry system using ZigBee technology for automatic and real-time monitoring of Biomedical signals. These signals are collected and processed using 2-tiered subsystems. The first subsystem is the mobile device which is carried on the body and runs a number of biosensors. The second subsystem performs further processing by a local base station using the raw data which is transmitted on-request by the mobile device. The processed data as well as its analysis are then continuously monitored and diagnosed through a human-machine interface. The system should possess low power consumption, low cost and advanced configuration possibilities. This paper accelerates the digital convergence age through continual research and development of technologies related to healthcare. 

Advanced wireless telemetry system for real-time surface readout during drill stem testing

2011 ◽  
Vol 51 (1) ◽  
pp. 95
Author(s):  
Sulaiman Sidek ◽  
Woo Hsuan Thai ◽  
Maharon Bin Jadid ◽  
Shangkar Venugopal ◽  
Suresh Marimuthu ◽  
...  
Keyword(s):  
Real Time ◽  
Acoustic Energy ◽  
Telemetry System ◽  
Skin Damage ◽  
Time Data ◽  
Drill Stem ◽  
Wireless Telemetry ◽  
Time Regime ◽  
Jack Up ◽  
Bottomhole Pressure

Drill stem testing (DST) provides reservoir information that helps evaluate the potential of a new field. The data includes permeability, total skin (damage) and formation pressure, but these calculations are possible only if the build-up period is sufficiently long to attain middle time regime. The best technique for determining the length of flowing and build-up periods required is to monitor real-time bottomhole pressure (BHP) at surface. Traditionally, BHP and temperature data have been recorded using downhole memory gauges, but the data could only be retrieved after the test had concluded and the DST bottomhole assembly (BHA) was pulled out of hole. Wireline surface read-out (SRO) was used in the next evolution of the applicable technology. This method lowered a wireline retrieval tool into the BHA during the build-up periods to retrieve real-time data from downhole gauges. This technique worked satisfactorily during build-up periods but was difficult to achieve during flowing periods, especially at high rates and with sand or solid production. Now, a real-time downhole data acquisition solution that uses the newest generation of an acoustic wireless telemetry system has been developed. This system allows data transmission by the tubular wall using acoustic energy during flowing and build-up periods, thus providing real-time SRO throughout the test to facilitate quick decisions and troubleshooting solutions. With the acoustic wireless telemetry system, wireline intervention during DST is not required, thus eliminating inherent risks and costs of such operations. A project undertaken by PETRONAS and a major service company using this system was successfully implemented on jack-up rigs in Malaysia. This paper discusses the challenges and step-by-step improvements made to enable these jobs to successfully meet the sought-after goals.

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