Gas Production Optimization Using Thermodynamics Hydrate Inhibition Flow Assurance Method

2019 ◽  
Author(s):  
Kenechukwu Obiajulu Nwankwo
Keyword(s):  
Gas Production ◽  
Production Optimization ◽  
Flow Assurance

scholarly journals Evaluation of natural gas production optimization in Kailashtila gas field in Bangladesh using decline curve analysis method

2015 ◽  
Vol 50 (1) ◽  
pp. 29-38 ◽  
Author(s):  
MS Shah ◽  
HMZ Hossain
Keyword(s):  
Production System ◽  
Gas Production ◽  
Curve Analysis ◽  
Production Optimization ◽  
Reservoir Pressure ◽  
Gas Field ◽  
Wellhead Pressure ◽  
Decline Curve Analysis ◽  
Decline Curve ◽  
Overall Performance

Decline curve analysis of well no KTL-04 from the Kailashtila gas field in northeastern Bangladesh has been examined to identify their natural gas production optimization. KTL-04 is one of the major gas producing well of Kailashtila gas field which producing 16.00 mmscfd. Conventional gas production methods depend on enormous computational efforts since production systems from reservoir to a gathering point. The overall performance of a gas production system is determined by flow rate which is involved with system or wellbore components, reservoir pressure, separator pressure and wellhead pressure. Nodal analysis technique is used to performed gas production optimization of the overall performance of the production system. F.A.S.T. Virtu Well™ analysis suggested that declining reservoir pressure 3346.8, 3299.5, 3285.6 and 3269.3 psi(a) while signifying wellhead pressure with no changing of tubing diameter and skin factor thus daily gas production capacity is optimized to 19.637, 24.198, 25.469, and 26.922 mmscfd, respectively.Bangladesh J. Sci. Ind. Res. 50(1), 29-38, 2015

An ESP Production Optimization Algorithm Applied to Unconventional Wells

2021 ◽  
Author(s):  
Fernando Bermudez ◽  
Noor Al Nahhas ◽  
Hafsa Yazdani ◽  
Michael LeTan ◽  
Mohammed Shono
Keyword(s):  
Gas Production ◽  
Oil Wells ◽  
Production Optimization ◽  
Operating Conditions ◽  
Tight Oil ◽  
Daily Production ◽  
Free Gas ◽  
Maximum Production ◽  
Electrical Submersible Pumps ◽  
High Production

Abstract The objectives and Scope is to evaluate the feasibility of a Production Maximization algorithm for ESPs on unconventional wells using projected operating conditions instead of current ones, which authors expect will be crucial in adjusting the well deliverability to optimum frequencies on the rapidly changing conditions of tight oil wells. Actual production data for an unconventional well was used, covering from the start of Natural Flow production up to 120 days afterwards. Simulating what the production would be if a VFD running on IMP Optimization algorithms had been installed, new values for well flowing pressures were calculated, daily production scenarios were evaluated, and recommended operating frequencies were plotted. Result, observations, and conclusions: A. Using the Intelligent Maximum Production (IMP) algorithm allows maximum production from tight oil wells during the initial high production stage, and the prevention of gas-locking at later stages when gas production increases. B. The adjustment of frequency at later stages for GOR wells is key to maintaining maximum production while controlling free gas at the intake when compared against controlling the surface choke. Novel/additive information: The use of Electrical Submersible Pumps for the production of unconventional wells paired with the use of a VFD and properly designed control algorithms allows faster recovery of investment by pumping maximum allowable daily rates while constraining detrimental conditions such as free gas at the intake.

AI for Production Forecasting and Optimization of Gas Wells: A Case Study on a Middle-East Gas Field

2021 ◽  
Author(s):  
Jimmy Thatcher ◽  
Abdul Rehman ◽  
Ivan Gee ◽  
Morgan Eldred
Keyword(s):  
Machine Learning ◽  
Optimal Parameter ◽  
Gas Production ◽  
Production Optimization ◽  
Machine Learning Techniques ◽  
Gas Wells ◽  
Optimal Production ◽  
Unsupervised Machine Learning ◽  
Production Forecasting ◽  
Run Time

Abstract Oil & Gas extraction companies are using a vast amount of capital and expertise on production optimization. The scale and diversity of information required for analysis is massive and often leading to a prioritization between time and precision for the teams involved in the process. This paper provides a success story of how artificial intelligence (AI) is used to dynamically and effeciently optimize and predict production of gas wells. In particular, we focus on the application of unsupervised machine learning to identify under different potential constraints the optimal production parameter settings that can lead to maximum production. A machine learning model is supported by a decision support system that can enhance future drilling operations and also help answer important questions such as why a particular well or group of wells is producing differently than others of the same type or what kind of parameters that work on different wells in different conditions. The model can be advanced to optimize within field constraints such as facility handling capacity, quotas, budget or emmisions. The methods used were a combination of similarity measures and unsupervised machine learning techniques which were effective in identifying wells and clusters of wells that have similar production and behavioral profiles. The clusters of wells were then used to identify the process path (specific drilling and completion, choke size, chemicals, etc processes) most likely to result in optimal production and to identify the most impactful variables on production rate or cumulative production via an additional clustering of the principle charactersitics of the well. The data sets used to build these models include but are not limited to gas production data (daily volume), drilling data (well logs, fluid summary etc.), completion data (frac, cement bond logs), and pre-production testing data (choke, pressure etc.) Initial results indicate that this approach is a feasible approach, on target in terms of accuracy with traditional methods and represents a novel, data driven, method of identifying optimal parameter settings for desired production levels; with the ability to perform forecasts and optimization scenarios in run-time. The approach of using machine learning for production forecasting and production optimization in run-time has immense values in terms of the ability to augment domain expertise and create detailed studies in a fraction of the time that is typically required using traditional approaches. Building on same approach to optimise the field to deliver most reliable or most effeciently against a parameter will be an invaluable feature for overall asset optimisation.

In-House Software Development for Gas Production Optimization: A South East Asia Perspective

2018 ◽  
Author(s):  
Chayut Wongkamthong ◽  
Kongphop Wongpattananukul ◽  
Chaiyaporn Suranetinai ◽  
Varoon Vongsinudom ◽  
Peerapong Ekkawong
Keyword(s):  
Software Development ◽  
East Asia ◽  
Gas Production ◽  
Production Optimization ◽  
South East Asia
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