Innovative for Global Production Optimization Techniques of Kalimantan Oil Field with Gas Lift Allocation Under Limited Gas Supply Using Equal Slope Method

2018 ◽  
Author(s):  
W. P. Anggara
Keyword(s):  
Optimization Techniques ◽  
Production Optimization ◽  
Oil Field ◽  
Slope Method ◽  
Gas Lift ◽  
Gas Supply

First Digital Intelligent Artificial Lift Production Optimization Technology in UAE Dual-String Gas Lift Well - Business Case and Implementation Plan

2019 ◽  
Author(s):  
Ahmed Alshmakhy ◽  
Khadija Al Daghar ◽  
Sameer Punnapala ◽  
Shamma AlShehhi ◽  
Abdel Ben Amara ◽  
...  
Keyword(s):  
Business Case ◽  
Production Optimization ◽  
Implementation Plan ◽  
Artificial Lift ◽  
Gas Lift

scholarly journals Injection-production optimization of carbonate reservoir based on an inter-well connectivity model

2021 ◽  
pp. 014459872199465
Author(s):  
Yuhui Zhou ◽  
Sheng Lei ◽  
Xuebiao Du ◽  
Shichang Ju ◽  
Wei Li
Keyword(s):  
History Matching ◽  
Water Injection ◽  
Field Application ◽  
Carbonate Reservoir ◽  
Production Optimization ◽  
Oil Field ◽  
Carbonate Reservoirs ◽  
Utilization Rate ◽  
Production Volume ◽  
Connectivity Model

Carbonate reservoirs are highly heterogeneous. During waterflooding stage, the channeling phenomenon of displacing fluid in high-permeability layers easily leads to early water breakthrough and high water-cut with low recovery rate. To quantitatively characterize the inter-well connectivity parameters (including conductivity and connected volume), we developed an inter-well connectivity model based on the principle of inter-well connectivity and the geological data and development performance of carbonate reservoirs. Thus, the planar water injection allocation factors and water injection utilization rate of different layers can be obtained. In addition, when the proposed model is integrated with automatic history matching method and production optimization algorithm, the real-time oil and water production can be optimized and predicted. Field application demonstrates that adjusting injection parameters based on the model outputs results in a 1.5% increase in annual oil production, which offers significant guidance for the efficient development of similar oil reservoirs. In this study, the connectivity method was applied to multi-layer real reservoirs for the first time, and the injection and production volume of injection-production wells were repeatedly updated based on multiple iterations of water injection efficiency. The correctness of the method was verified by conceptual calculations and then applied to real reservoirs. So that the oil field can increase production in a short time, and has good application value.

Numerical Simulation of Gas Lift Optimization Using Artificial Intelligence for a Middle Eastern Oil Field

2021 ◽  
Author(s):  
Mohammed Ahmed Al-Janabi ◽  
Omar F. Al-Fatlawi ◽  
Dhifaf J. Sadiq ◽  
Haider Abdulmuhsin Mahmood ◽  
Mustafa Alaulddin Al-Juboori
Keyword(s):  
Genetic Algorithm ◽  
Sensitivity Analysis ◽  
Middle Eastern ◽  
Optimization Technique ◽  
Injection Rate ◽  
Oil Field ◽  
Oil Fields ◽  
Reservoir Pressure ◽  
Artificial Lift ◽  
Gas Lift

Abstract Artificial lift techniques are a highly effective solution to aid the deterioration of the production especially for mature oil fields, gas lift is one of the oldest and most applied artificial lift methods especially for large oil fields, the gas that is required for injection is quite scarce and expensive resource, optimally allocating the injection rate in each well is a high importance task and not easily applicable. Conventional methods faced some major problems in solving this problem in a network with large number of wells, multi-constrains, multi-objectives, and limited amount of gas. This paper focuses on utilizing the Genetic Algorithm (GA) as a gas lift optimization algorithm to tackle the challenging task of optimally allocating the gas lift injection rate through numerical modeling and simulation studies to maximize the oil production of a Middle Eastern oil field with 20 production wells with limited amount of gas to be injected. The key objective of this study is to assess the performance of the wells of the field after applying gas lift as an artificial lift method and applying the genetic algorithm as an optimization algorithm while comparing the results of the network to the case of artificially lifted wells by utilizing ESP pumps to the network and to have a more accurate view on the practicability of applying the gas lift optimization technique. The comparison is based on different measures and sensitivity studies, reservoir pressure, and water cut sensitivity analysis are applied to allow the assessment of the performance of the wells in the network throughout the life of the field. To have a full and insight view an economic study and comparison was applied in this study to estimate the benefits of applying the gas lift method and the GA optimization technique while comparing the results to the case of the ESP pumps and the case of naturally flowing wells. The gas lift technique proved to have the ability to enhance the production of the oil field and the optimization process showed quite an enhancement in the task of maximizing the oil production rate while using the same amount of gas to be injected in the each well, the sensitivity analysis showed that the gas lift method is comparable to the other artificial lift method and it have an upper hand in handling the reservoir pressure reduction, and economically CAPEX of the gas lift were calculated to be able to assess the time to reach a profitable income by comparing the results of OPEX of gas lift the technique showed a profitable income higher than the cases of naturally flowing wells and the ESP pumps lifted wells. Additionally, the paper illustrated the genetic algorithm (GA) optimization model in a way that allowed it to be followed as a guide for the task of optimizing the gas injection rate for a network with a large number of wells and limited amount of gas to be injected.

Artificial Intelligence Assisted Well Portfolio Optimization - An Automated Reservoir Management Advisory System to Maximize the Asset Value - Case Study from ADNOC Onshore

2021 ◽  
Author(s):  
Subba Ramarao Rachapudi Venkata ◽  
Nagaraju Reddicharla ◽  
Shamma Saeed Alshehhi ◽  
Indra Utama ◽  
Saber Mubarak Al Nuimi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hybrid Approach ◽  
Simulation Models ◽  
Production Optimization ◽  
Added Value ◽  
Risk Scores ◽  
Well Performance ◽  
Field Development ◽  
Advisory System ◽  
Gas Lift

Abstract Matured hydrocarbon fields are continuously deteriorating and selection of well interventions turn into critical task with an objective of achieving higher business value. Time consuming simulation models and classical decision-making approach making it difficult to rapidly identify the best underperforming, potential rig and rig-less candidates. Therefore, the objective of this paper is to demonstrate the automated solution with data driven machine learning (ML) & AI assisted workflows to prioritize the intervention opportunities that can deliver higher sustainable oil rate and profitability. The solution consists of establishing a customized database using inputs from various sources including production & completion data, flat files and simulation models. Automation of Data gathering along with technical and economical calculations were implemented to overcome the repetitive and less added value tasks. Second layer of solution includes configuration of tailor-made workflows to conduct the analysis of well performance, logs, output from simulation models (static reservoir model, well models) along with historical events. Further these workflows were combination of current best practices of an integrated assessment of subsurface opportunities through analytical computations along with machine learning driven techniques for ranking the well intervention opportunities with consideration of complexity in implementation. The automated process outcome is a comprehensive list of future well intervention candidates like well conversion to gas lift, water shutoff, stimulation and nitrogen kick-off opportunities. The opportunity ranking is completed with AI assisted supported scoring system that takes input from technical, financial and implementation risk scores. In addition, intuitive dashboards are built and tailored with the involvement of management and engineering departments to track the opportunity maturation process. The advisory system has been implemented and tested in a giant mature field with over 300 wells. The solution identified more techno-economical feasible opportunities within hours instead of weeks or months with reduced risk of failure resulting into an improved economic success rate. The first set of opportunities under implementation and expected a gain of 2.5MM$ with in first one year and expected to have reoccurring gains in subsequent years. The ranked opportunities are incorporated into the business plan, RMP plans and drilling & workover schedule in accordance to field development targets. This advisory system helps in maximizing the profitability and minimizing CAPEX and OPEX. This further maximizes utilization of production optimization models by 30%. Currently the system was implemented in one of ADNOC Onshore field and expected to be scaled to other fields based on consistent value creation. A hybrid approach of physics and machine learning based solution led to the development of automated workflows to identify and rank the inactive strings, well conversion to gas lift candidates & underperforming candidates resulting into successful cost optimization and production gain.

A Success Story in Managing and Optimising Gas Lift Wells in Matured Oil Field: Automated Workflows in Digital Fields as Enablers to Accelerate Opportunities Creation and Production Optimisation

2021 ◽  
Author(s):  
Amir Badzly M. Nazri ◽  
W. M. Anas W. Khairul Anuar ◽  
Lucas Ignatius Avianto Nasution ◽  
Hayati Turiman ◽  
Shar Kawi Hazim Shafie ◽  
...  
Keyword(s):  
Model Validation ◽  
Field Performance ◽  
Oil Field ◽  
Working Environment ◽  
Key Factors ◽  
Collaborative Working ◽  
Water Cut ◽  
Integrated Operations ◽  
Collaborative Working Environment ◽  
Gas Lift

Abstract Field S located in offshore Malaysia had been producing for more than 30 years with nearly 90% of current active strings dependent on gas lift assistance. Subsurface challenges encountered in this matured field such as management of increasing water-cut, sand production, and depleting reservoir pressure are one of key factors that drive the asset team to continuously monitor the performance of gaslifted wells to ensure better control of production thereby meeting target deliverability of the field. Hence, Gas Lift Optimization (GLOP) campaign was embarked in Field S to accelerate short term production with integration of Gas Lift Management Modules in Integrated Operations (IO). A workflow was created to navigate asset team in this campaign from performing gaslift health check, diagnostic and troubleshooting to data and model validation until execution prior to identification of GLOP candidates with facilitation from digital workflows. Digital Fields and Integrated Operations (IO) developed in Field S provided an efficient collaborative working environment to monitor field performance real time and optimize production continuously. Digital Fields comprises of multiple engineering workflows developed and operationalized to act as enablers for the asset team to quickly identify the low-hanging fruit opportunities. This paper will focus on entire cycle process of digital workflows with engineer's intervention in data hygiene and model validation, the challenges to implement GLOP, and results from the campaign in Field S.

Samotlor: Implementation of Real Time Production Optimization Process for the Largest Russian Oil Field

2011 ◽  
Author(s):  
Sergey Shevchenko ◽  
Dmitry Mironov ◽  
V.A. Navozov ◽  
Eduard Muslimov ◽  
Roman Leonidovich Pchelnikov ◽  
...  
Keyword(s):  
Real Time ◽  
Production Optimization ◽  
Oil Field ◽  
Optimization Process ◽  
Time Production

Production Optimization over 20% with Artificial Lift ESP Pilot Systems in Mature Oil Field, Case Study from Middle East

2014 ◽  
Author(s):  
Hector Aguilar ◽  
Aref Almarzooqi ◽  
Tarek Mohamed El Sonbaty ◽  
Leigber Villarreal
Keyword(s):  
Middle East ◽  
Production Optimization ◽  
Oil Field ◽  
Field Case ◽  
Artificial Lift

scholarly journals Production Optimization for Natural Flow and ESP Well A Case Study on Well NS-5 Mishrif Formation-Nasriya Oil Field

2020 ◽  
Vol 10 (2) ◽  
pp. 17-35
Author(s):  
Hamzah Amer Abdulameer ◽  
Dr. Sameera Hamd-Allah
Keyword(s):  
Production Rate ◽  
Production Optimization ◽  
Oil Field ◽  
Reservoir Pressure ◽  
Pump Frequency ◽  
Individual Parameter ◽  
Well Production ◽  
Well Design ◽  
Natural Flow ◽  
Reservoir Conditions

As the reservoir conditions are in continuous changing during its life, well production rateand its performance will change and it needs to re-model according to the current situationsand to keep the production rate as high as possible.Well productivity is affected by changing in reservoir pressure, water cut, tubing size andwellhead pressure. For electrical submersible pump (ESP), it will also affected by numberof stages and operating frequency.In general, the production rate increases when reservoir pressure increases and/or water cutdecreases. Also the flow rate increase when tubing size increases and/or wellhead pressuredecreases. For ESP well, production rate increases when number of stages is increasedand/or pump frequency is increased.In this study, a nodal analysis software was used to design one well with natural flow andother with ESP. Reservoir, fluid and well information are taken from actual data of Mishrifformation-Nasriya oil field/ NS-5 well. Well design steps and data required in the modelwill be displayed and the optimization sensitivity keys will be applied on the model todetermine the effect of each individual parameter or when it combined with another one.

scholarly journals Analysis of Siphon String Application to Optimize Gas Lift Injection

2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Sofani Muflih ◽  
Silvya Dewi Rahmawati
Keyword(s):  
Flow Regime ◽  
Production Rate ◽  
Flow Condition ◽  
Production Optimization ◽  
Font Size ◽  
Additional Advantage ◽  
Offshore Area ◽  
Well Model ◽  
Production Period ◽  
Gas Lift

<p><span style="font-size: small;"><span style="font-family: Times New Roman;"><em>B-</em><em>X</em><em> well is an oil producing well at Bravo field in Natuna offshore area, which was completed at IBS zone using 5-1/2 inch tubing size. </em><em>However, after several years of production period, the well’s production rate decreased due to reservoir depletion, and experienced gas lift performance problem indicated by unstable flowing condition (slugging flow). In year 2020, Siphon String installation is applied to the well in order to give deeper point of gas lift injection and better well’s production. The additional advantage by having smaller tubing size (insert tubing) is to reduce the slugging flow condition. The analysis of this siphon string installation at the B-X well, technically will be performed by evaluating gas lift performance and the flow regime inside the tubing using a Well Model simulator. The simulation was developed based on the real well condition. Several sensitivity analysis were done through several cases such as: variation in depth of gas lift point of injection, and the length of the siphon string. The simulation was required to evaluate the effectiveness of the existing installation, and to give better recommendation for the other well that has the same problem.  The result indicates that the depth of the current siphon string installation has been providing the optimum production rate, while the slugging flow condition will still be occurred at any given scenario of the siphon string depth due to the very low of well’s productivity. The similar procedure and evaluation can be implemented to other oil wells using gas lift injection located either in offshore or onshore field. </em></span></span></p><p><em><span style="font-family: Times New Roman; font-size: small;"> </span></em></p><p><em><span style="font-family: Times New Roman; font-size: small;">Keywords: Production Optimization, Siphon String, Flow Regime</span></em></p>

Sign in / Sign up

Export Citation Format

Share Document