Production Optimization Challenges of Gas Wells with Liquid Loading Problem Using Foaming Agents

Author(s):  
Snezana Sevic ◽  
Miso Solesa
2019 ◽  
Author(s):  
Mohammed Bashir Abdullahi ◽  
A. D. I Sulaiman ◽  
Usman Abdulkadir ◽  
Ibraheem Salaudeen ◽  
Bashir Umar Shehu

2021 ◽  
Vol 26 (3) ◽  
pp. 245
Author(s):  
Chuan Xie ◽  
Chunyu Xie ◽  
Yulong Zhao ◽  
Liehui Zhang ◽  
Yonghui Liu ◽  
...  

2018 ◽  
Vol 60 ◽  
pp. 153-163 ◽  
Author(s):  
Zhennan Zhang ◽  
Baojiang Sun ◽  
Zhiyuan Wang ◽  
Yonghai Gao ◽  
Shujie Liu ◽  
...  

2021 ◽  
Vol 73 (07) ◽  
pp. 57-57
Author(s):  
Leonard Kalfayan

As unconventional oil and gas fields mature, operators and service providers are looking toward, and collaborating on, creative and alternative methods for enhancing production from existing wells, especially in the absence of, or at least the reduction of, new well activity. While oil and gas price environments remain uncertain, recent price-improvement trends are supporting greater field testing and implementation of innovative applications, albeit with caution and with cost savings in mind. Not only is cost-effectiveness a requirement, but cost-reducing applications and solutions can be, too. Of particular interest are applications addressing challenging well-production needs such as reducing or eliminating liquid loading in gas wells; restimulating existing, underperforming wells, including as an alternative to new well drilling and completion; and remediating water blocking and condensate buildup, both of which can impair production from gas wells severely. The three papers featured this month represent a variety of applications relevant to these particular well-production needs. The first paper presents a technology and method for liquid removal to improve gas production and reserves recovery in unconventional, liquid-rich reservoirs using subsurface wet-gas compression. Liquid loading, a recurring issue downhole, can severely reduce gas production and be costly to remediate repeatedly, which can be required. This paper discusses the full technology application process and the supportive results of the first field trial conducted in an unconventional shale gas well. The second paper discusses the application of the fishbone stimulation system and technique in a tight carbonate oil-bearing formation. Fishbone stimulation has been around for several years now, but its best applications and potential have not necessarily been fully understood in the well-stimulation community. This paper summarizes a successful pilot application resulting in a multifold increase in oil-production rate and walks the reader through the details of the pilot candidate selection, completion design, operational challenges, and lessons learned. The third paper introduces and proposes a chemical treatment to alleviate phase trapping in tight carbonate gas reservoirs. Phase trapping can be in the form of water blocking or increasing condensate buildup from near the wellbore and extending deeper into the formation over time. Both can reduce relative permeability to gas severely. Water blocks can be a one-time occurrence from drilling, completion, workover, or stimulation operations and can often be treated effectively with solvent plus proper additive solutions. Similar treatments for condensate banking in gas wells, however, can provide only temporary alleviation, if they are even effective. This paper proposes a technique for longer-term remediation of phase trapping in tight carbonate gas reservoirs using a unique, slowly reactive fluid system. Recommended additional reading at OnePetro: www.onepetro.org. SPE 200345 - Insights Into Field Application of Enhanced-Oil-Recovery Techniques From Modeling of Tight Reservoirs With Complex High-Density Fracture Network by Geng Niu, CGG, et al. SPE 201413 - Diagnostic Fracture Injection Test Analysis and Simulation: A Utica Shale Field Study by Jeffery Hildebrand, The University of Texas at Austin, et al.


2021 ◽  
Author(s):  
Jimmy Thatcher ◽  
Abdul Rehman ◽  
Ivan Gee ◽  
Morgan Eldred

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.


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