Integrated Asset Modeling in Mature Offshore Fields: Challenges and Successes

2014 ◽  
Author(s):  
R.. Nazarov ◽  
P.. Zalama ◽  
M.. Hernandez ◽  
C.. Rivas
Keyword(s):  
Production Management ◽  
Treatment Plant ◽  
Integrated Model ◽  
Production Optimization ◽  
Production Model ◽  
Peak Performance ◽  
Fluid Composition ◽  
Well Performance ◽  
System Pressure ◽  
Gas Lift

Abstract Production management in mature fields is a very challenging task which involves a multidisciplinary technical approach to minimize the decline rate and extend the life of the asset/field. Most of the time Integrated Asset Modeling (IAM) techniques are applied to green fields with main objectives of identifying the “bottlenecks” or to forecast production with different development cases. In the case of mature fields it is mostly considered as an optional study with less analytical value due to low operating surface pressures, already existing facilities, known well performance and studied reservoir geology. Nevertheless the processing of the reservoir, production and operational data in mature assets through one integrated workflow facilitates field management overall, thereby helping in the estimation of the remaining reserves and indicating real opportunities for optimization not seen by initial engineering scenarios. Additionally, IAM should be incorporated before getting to EOR studies. This paper describes the applied reservoir engineering workflow and integrated production model for the TSP fields (Teak, Samaan and Poui) located in the South East of Trinidad. TSP fields are jointly owned by by Repsol (70%), Petrotrin (15%) and NGC (15%) and are operated by Repsol. Current production of TSP is 13, 500 bopd. The oil produced from these fields is generally light oil, with an average range of 25-40 API and a solution GOR 200-1400scf/stb. Gas lift is the artificial lift system used in 95% of the wells. Average water cut is around 85%. Interaction of Production Engineering, Subsurface, Drilling, HSE, Facilities, and Maintenance departments is the key aspect to sustain the efficient operability of the TSP fields and operate at peak performance in spite of ageing installations, flow assurance problems and depleted reservoirs. The implementation of Operated Asset Structure in TSP in 2013 reinforced the cooperation between departments to achieve the main goals: minimum production deferrals, production optimization, screening of new opportunities and reserves, process improvement, facilities maintenance and effective logistics. Additionally, the Integrated Asset Modeling has been incorporated as part of the engineering surveillance which includes 3 fields, 100 wells, gas lift injection network, gas compressors, water treatment plant, etc. Real data from different sources and platforms, such as pressure temperature sensors, daily measured well parameters, reported operational figures, monthly welltests and screened remaining reserves are jointly transferred to the integrated model, built in commercial software (GAP/RESOLVE), bringing the field data processing and production management to the state-of-the-art level. Gas lift volume availability and system pressure, performed rigless intervention jobs (including recompletion of new zones), change of the fluid composition in certain wells, reconfiguration of facilities are timely reflected in the TSP integrated model. Based on the sensitivity runs and output results immediate actions are taken to comply with the production target.

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.

scholarly journals The Critical Investigation on Essential Parameters to Optimize the Gas Lift Performance In “J” Field Using Prosper Modelling

2018 ◽  
Vol 7 (2) ◽  
pp. 46-54
Author(s):  
Fitrianti Fitrianti ◽  
Dike Fitriansyah Putra ◽  
Desma Cendra
Keyword(s):  
Well Being ◽  
Production Optimization ◽  
Productivity Index ◽  
Injection System ◽  
Driving Mechanism ◽  
Well Performance ◽  
Artificial Lift ◽  
Index Value ◽  
Gas Lift ◽  
Selection Of

The declining reservoir, oil production and pressure depletion with the well being produced, the results of the investment of the well will also decrease. For that there needs to be energy that can help to lift the fluid to the surface. One of the artificial lift methods that can be used is a gas lift. Gas lift is a method commonly used when there is a natural gas source as an injection gas supply. The selection of the artificial lift method is based on several considerations, namely the reservoir conditions, fluid conditions, well conditions, conditions on the surface, availability of electricity, availability of gas, and sand problem. The influential parameters in the selection of gas lifts include: Productivity Index (PI), Gas Liquid Ratio (GLR), depth of the well and driving mechanism from the reservoir. The Gas Lift that the production optimization wants to do is the injection system in a Continuous Gas Lift. Used in wells that have a high Productifity Index value. Where in the LB field to be analyzed, the Productifity Index value is 2.0 bpd / psi. This study intends to optimize a gaslift well performance as an effort to maximize the results of well production. Based on the research that has been done using Prosper Modeling on the “J” field, the following conclusions are obtained the effect of pressure and viscosity on the gas lift well flow rate in this condition can be said to be efficient, because the conditions / pressure given at temperatures below 300 F can reach the miscible condition and from the results of determining the optimal conditions to get the best well performance, obtain an optimal liquid rate of 1829.4 STB / D with an oil rate of 36.6 STB / D.   Keywords: Gas lift, Optimization, Immiscible Pressure, Viscosity

Stabilisation of Gaslift Production Using a Dynamic Simulator

2001 ◽  
Author(s):  
Yvonne V. Roberts ◽  
Matt Nicol
Keyword(s):  
Production Management ◽  
Mechanical Damage ◽  
Phase Changes ◽  
Fluid Composition ◽  
Remedial Action ◽  
Original Design ◽  
The North ◽  
Water Cut ◽  
The Impact ◽  
Gas Lift

Abstract A common problem with gas lifted wells is the development, over time, of instabilities in the injection/production behaviour. The question raised is initially that of “probable cause and effect”; the understanding of which is essential to the determination of possible remedial action. The major causes of unstable behaviour fall into three broad categories: • Design related - the original design is inappropriate or inflexible. • Mechanical - damage to, and/or failure of, valves and equipment. • Dynamic flow behaviour - changes in fluid composition and/or phase changes. Commonly, the instability incorporates elements from more than one category. This paper discusses one case in which a horizontal well in the North Sea, which had a gas lift completion designed for operation at a water cut of around 20%, exhibited unstable production after a rapid rise in water cut to approximately 80%. The paper shows how a new and unique dynamic gas lift simulator was used to reproduce the observed well behaviour, and how the model was then used to recommend remedial action to stabilise production. The impact of these remedial actions is discussed in the context of the overall production management. Finally, the implementation of the recommendations and the subsequent well behaviour are presented.

Machine-Learning Techniques Assist Data-Driven Well-Performance Optimization

2021 ◽  
Vol 73 (10) ◽  
pp. 63-64
Author(s):  
Chris Carpenter
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Performance Optimization ◽  
Production Optimization ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Well Performance ◽  
Learning Techniques ◽  
Expected Performance ◽  
Gas Lift

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 201696, “Robust Data-Driven Well-Performance Optimization Assisted by Machine-Learning Techniques for Natural-Flowing and Gas-Lift Wells in Abu Dhabi,” by Iman Al Selaiti, Carlos Mata, SPE, and Luigi Saputelli, SPE, ADNOC, et al., prepared for the 2020 SPE Annual Technical Conference and Exhibition, originally scheduled to be held in Denver, Colorado, 5–7 October. The paper has not been peer reviewed. Despite being proven to be a cost-effective surveillance initiative, remote monitoring is still not adopted in more than 60% of oil and gas fields around the world. Understanding the value of data through machine-learning (ML) techniques is the basis for establishing a robust surveillance strategy. In the complete paper, the authors develop a data-driven approach, enabled by artificial-intelligence methodologies including ML, to find an optimal operating envelope for gas-lift wells. Real-Time Well-Performance Optimization Wellsite Measurement and Control. - Flow Tests. - Past tests include sporadic measurement of multiphase rates and the associated flowing pressure and temperature, collected at various points of the production system, from bottomhole to separator conditions. Flow tests are also known as well tests; however, the authors use the term “flow test” in this paper to avoid confusion with well testing as used in pressure transient tests, including temporary shut-in pressure buildups (for producers) and pressure falloff tests (for injectors). Normally, a well would have limited data points from the past well tests (i.e., less than 50 valid flow tests in a period of 5–10 years). This data is the basis of creating ML models. Continuous Monitoring. - Every well should have adequate instrumentation, and its supporting infrastructure should include reliable power supply, minimum latency telemetry, and desktop access to production parameters. Alignment among real-time data and relational databases is required. Remote Control and Automated Actuation. - In addition to controllable valves, every well should be enabled with actuators to control the process variables. Remote control allows the operator to make changes to the current well-site configuration. Regulatory and Supervisory Control. - The value of automated closed-loop regulatory and supervisory control is to sustain optimal production while providing high well availability. Real-Time Production Optimization. - Continuous production optimization means that expected performance is challenged frequently by updating an optimal forecast with upper-level targets and current asset status. This is achieved by applying actions that close the gap between actual and expected performance. Faster surveillance loops compare actual vs. expected performance to determine minute, hourly, and daily gaps. A slower surveillance loop updates the asset’s expected performance. Well-Management Guidelines. - These are established, known limits to address and honor restrictions such as concession-contract obligations and legal limits, optimal reservoir management, flow assurance, economics, safety, and integrity.

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

Integrated Production Model as a Tool for Optimization the Development Strategy of the Sakhalin Oil and Gas Condensate Field

2021 ◽  
Author(s):  
Oleksandr Doroshenko ◽  
Miljenko Cimic ◽  
Nicholas Singh ◽  
Yevhen Machuzhak
Keyword(s):  
History Matching ◽  
Effective Field ◽  
Production Optimization ◽  
Production Model ◽  
Production Forecast ◽  
Hydrocarbon Production ◽  
Integrated Production ◽  
Wellhead Pressure ◽  
Field Development ◽  
The Impact

Abstract A fully integrated production model (IPM) has been implemented in the Sakhalin field to optimize hydrocarbons production and carried out effective field development. To achieve our goal in optimizing production, a strategy has been accurately executed to align the surface facilities upgrade with the production forecast. The main challenges to achieving the goal, that we have faced were:All facilities were designed for early production stage in late 1980's, and as the asset outdated the pipeline sizes, routing and compression strategies needs review.Detecting, predicting and reducing liquid loading is required so that the operator can proactively control the hydrocarbon production process.No integrated asset model exists to date. The most significant engineering tasks were solved by creating models of reservoirs, wells and surface network facility, and after history matching and connecting all the elements of the model into a single environment, it has been used for the different production forecast scenarios, taking into account the impact of infrastructure bottlenecks on production of each well. This paper describes in detail methodology applied to calculate optimal well control, wellhead pressure, pressure at the inlet of the booster compressor, as well as for improving surface flowlines capacity. Using the model, we determined the compressor capacity required for the next more than ten years and assessed the impact of pipeline upgrades on oil gas and condensate production. Using optimization algorithms, a realistic scenario was set and used as a basis for maximizing hydrocarbon production. Integrated production model (IPM) and production optimization provided to us several development scenarios to achieve target production at the lowest cost by eliminating infrastructure constraints.

scholarly journals IMPACT OF GAS LIFT INJECTION RATE ON OIL PRODUCTION RATE

2021 ◽  
pp. 76-94
Author(s):  
Dr. Mohamed A. GH. Abdalsadig
Keyword(s):  
Real Time ◽  
Energy Demand ◽  
Oil And Gas ◽  
Injection Pressure ◽  
Production Operation ◽  
Well Performance ◽  
Time Data ◽  
Daily Work ◽  
Wellhead Pressure ◽  
Gas Lift

As worldwide energy demand continues to grow, oil and gas fields have spent hundreds of billions of dollars to build the substructures of smart fields. Management of smart fields requires integrating knowledge and methods in order to automatically and autonomously handle a great frequency of real-time information streams gathered from those wells. Furthermore, oil businesses movement towards enhancing everyday production skills to meet global energy demands signifies the importance of adapting to the latest smart tools that assist them in running their daily work. A laboratory experiment was carried out to evaluate gas lift wells performance under realistic operations in determining reservoir pressure, production operation point, injection gas pressure, port size, and the influence of injection pressure on well performance. Lab VIEW software was used to determine gas passage through the Smart Gas Lift valve (SGL) for the real-time data gathering. The results showed that the wellhead pressure has a large influence on the gas lift performance and showed that the utilized smart gas lift valve can be used to enhanced gas Lift performance by regulating gas injection from down hole.

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>

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