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

Real Time Data: Interactive Monitoring of Data Flow for Continuous and Reliable Data Transmission

2013 ◽  
Author(s):  
Abdulrahman A. Al-Amer ◽  
Muhammad Al-Gosayir ◽  
Naser Al-Naser ◽  
Hussain Al-Towaileb
Keyword(s):  
Real Time ◽  
Data Transmission ◽  
Data Flow ◽  
Reliable Data ◽  
Time Data ◽  
Real Time Data

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.

Season Cycling Gas Storage in Stogit Fields. A Real-Time Data Transmission System

2021 ◽  
Author(s):  
Graciela Eva Naveda ◽  
France Dominique Louie ◽  
Corinna Locatelli ◽  
Julien Davard ◽  
Sara Fragassi ◽  
...  
Keyword(s):  
Natural Gas ◽  
Real Time ◽  
Fiber Optics ◽  
Data Transmission ◽  
Distribution Networks ◽  
Gas Storage ◽  
Transmission System ◽  
Lessons Learned ◽  
Time Data ◽  
Real Time Data

Abstract Natural gas has become one of the major sources of energy for homes, public buildings and businesses, therefore gas storage is particularly important to ensure continuous provision compensating the differences between supply and demand. Stogit, part of Snam group, has been carrying out gas storage activities since early 1960's. Natural gas is usually stored underground, in large storage reservoirs. The gas is injected into the porous rock of depleted reservoirs bringing the reservoir nearby to its original condition. Injected gas can be withdrawn depending on the need. Gas market demands for industries and homes in Italy are mostly guaranteed from those Stogit reservoirs even in periods when imports are in crisis. Typically, from April to October, the gas is injected in these natural reservoirs that are "geologically tested"; while from November to March, gas is extracted from the same reservoirs and pumped into the distribution networks to meet the higher consumer demand.  Thirty-eight (38) wells, across nine (9) depleted fields, are completed with downhole quartz gauges and some of them with fiber-optics gauges. Downhole gauges are installed to continuously measure and record temperature and pressure from multiple reservoirs. The Real Time data system installed for 29 wells is used to collect, transmit and make available downhole data to Stogit (Snam) headquarter office. Data is automatically collected from remote terminal units (RTUs) and transferred over Stogit (Snam) network. The entire system works autonomously and has the capability of being remotely managed from anywhere over the corporate Stogit (Snam) IT network. Historical trends, including fiber optics gauges ones, are visualized and data sets could be retrieved using a fast and user-friendly software that enables data import into interpretation and reservoir modeling software. The use of this data collection and transmission system, versus the traditional manual download, brought timely data delivery to multiple users, coupled with improved personnel safety since land travels were eliminated. The following pages describe the case study, lessons learned, and integrated new practices used to improve the current and future data transmission deployments.

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