Production Optimization for Second Stage Field Development Using ICD and Advanced Well Placement Technology

2008 ◽  
Author(s):  
D. Maggs ◽  
A.G. Raffn ◽  
Francisco Porturas ◽  
J. Murison ◽  
F. Tay ◽  
...  
Keyword(s):  
Production Optimization ◽  
Well Placement ◽  
Field Development ◽  
Second Stage

scholarly journals A field development strategy for the joint optimization of flow allocations, well placements and well trajectories

2020 ◽  
pp. 014459872097442
Author(s):  
M A Dada ◽  
M Mellal ◽  
A Makhloufi ◽  
H Belhouchet
Keyword(s):  
Genetic Algorithm ◽  
Development Strategy ◽  
Oil And Gas ◽  
Water Saturation ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Production Optimization ◽  
Well Placement ◽  
Field Development ◽  
Maximum Duration

One of the major goals that field planning engineers and decision makers have to achieve in terms of reservoir management and hydrocarbon recovery optimization is the maximization of return on financial investments. This task yet very challenging due to high number of decision variables and some uncertainties, pushes the engineers and technical advisors to seek for robust optimization methods in order to optimally place wells in the most profitable locations with a focus on increasing the net-present value over a project life-cycle. The quest to deliver a good quality advice is also dependent on how some uncertainties – geologic, economic and flow patterns – have been handled and formulated all along the optimization process. With the enhancement of computer power and the advent of remarkable optimization techniques, the oil and gas industry has at hand a wide range of tools to get an overview on value maximization from petroleum assets. Amongst these tools, genetic algorithms which belong to stochastic optimization methods have become well known in the industry as one the best alternatives to apply when trying to solve well placement and production allocation problems, though computationally demanding. The aim of this work is to present a novel approach in the area of hydrocarbon production optimization where control settings and well placement are to be determined based on a single objective function, in addition to the optimization of wells’ trajectories. Starting from a reservoir dynamic model of a synthetic offshore oil field assisted by water injection, the work consisted in building a data-driven model that was generated using artificial neural networks. Then, we used Matlab’s genetic algorithm toolbox to perform all the needed optimizations; from which, we were able to establish a drilling schedule for the set of wells to be realized, and we made it possible to simultaneously get the well location and configuration (vertical or horizontal), well type (producer or injector), well length, well orientation – in the horizontal plane –, as well as well controls (flow rates) and near wellbore pressure with respect to a set of linear and nonlinear-constraints. These constraints were formulated so as to reproduce real field development considerations, and with the aid of a genetic algorithm procedure written upon Matlab, we were able to satisfy those constraints such as, maximum production and injection rates, optimal wellbore pressures, maximum allowable liquid processing capacity, optimal well locations, wells’ drilling and completion maximum duration, in addition to other considerations. We have investigated some scenarios with the intention of proving the benefits of development strategy that we have chosen to study. It was found the chosen scenario could improve NPV by 3 folds in comparison to a base case scenario. The positioning of the wells was successful as all producers were placed in zones having initial water saturation less than 0.4., and all injectors were placed high water saturation zones. Moreover, we established a procedure regarding well trajectory design and optimization by taking into account, minimum dogleg severity and maximum duration for a well to be drilled and completed with respect to a time threshold. The findings as well as the workflow that will be presented hereafter could be considered as a guideline for subsequent tasks pertaining to the process of decision making, especially when it has to do with the development of green oil and gas fields and will certainly help in the placement of wells in less risky and cost-effective locations.

Well Placement Technique and Production Optimization for Mature Field - A Case Study of L-X Field

2020 ◽  
Vol 46 ◽  
pp. 168-179
Author(s):  
Patrick Nwafor ◽  
Kelani Bello
Keyword(s):  
Oil And Gas ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Oil And Gas Industry ◽  
Optimization Method ◽  
Simulation Software ◽  
Production Optimization ◽  
Direct Optimization ◽  
Well Placement ◽  
Local Optimal Solution

A Well placement is a well-known technique in the oil and gas industry for production optimization and are generally classified into local and global methods. The use of simulation software often deployed under the direct optimization technique called global method. The production optimization of L-X field which is at primary recovery stage having five producing wells was the focus of this work. The attempt was to optimize L-X field using a well placement technique.The local methods are generally very efficient and require only a few forward simulations but can get stuck in a local optimal solution. The global methods avoid this problem but require many forward simulations. With the availability of simulator software, such problem can be reduced thus using the direct optimization method. After optimization an increase in recovery factor of over 20% was achieved. The results provided an improvement when compared with other existing methods from the literatures.

Brenda Field Development: A Best Practice In Horizontal Well Placement Leading To Optimal Reservoir Drainage

2007 ◽  
Author(s):  
Ken E.T. Halward ◽  
Joe Emery ◽  
Rod Christensen ◽  
Daniel Joseph Bourgeois ◽  
Grant Skinner ◽  
...  
Keyword(s):  
Best Practice ◽  
Horizontal Well ◽  
Well Placement ◽  
Field Development

New method for Handling of Infrastructural Constraints for Integrated Modeling in Steady Case

2021 ◽  
Author(s):  
Dmitry Moiseevich Olenchikov
Keyword(s):  
Optimal Solution ◽  
Field Development ◽  
Flow Models ◽  
Second Stage ◽  
Operation Modes ◽  
Reservoir Flow ◽  
Terminal Constraints ◽  
Surface Network ◽  
Two Stages ◽  
And Control

Abstract Recently, more and more reservoir flow models are being extended to integrated ones to consider the influence of the surface network on the field development. A serious numerical problem is the handling of constraints in the form of inequalities. It is especially difficult in combination with optimization and automatic control of well and surface equipment. Traditional numerical methods solve the problem iteratively, choosing the operation modes for network elements. Sometimes solution may violate constraints or not be an optimal. The paper proposes a new flexible and relatively efficient method that allows to reliably handle constraints. The idea is to work with entire set of all possible operation modes according to constraints and control capabilities. Let's call this set an operation modes domain (OMD). The problem is solved in two stages. On the first stage (direct course) the OMD are calculated for all network elements from wells to terminal. Constraints are handled by narrowing the OMD. On the second stage (backward course) the optimal solution is chosen from OMD.

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.

Advanced Multi-Disciplinary Simulation Workflow for Well Placement, Hydraulic Fracturing and Production Optimization Applied to the Diyab Unconventional Play in Onshore UAE

2021 ◽  
Author(s):  
Hamid Pourpak ◽  
Samuel Taubert ◽  
Marios Theodorakopoulos ◽  
Arnaud Lefebvre-Prudencio ◽  
Chay Pointer ◽  
...  
Keyword(s):  
Hydraulic Fracturing ◽  
United Arab Emirates ◽  
Reservoir Characterization ◽  
Horizontal Wells ◽  
Production Optimization ◽  
Production Performance ◽  
Well Placement ◽  
Fully Integrated ◽  
Sector Model ◽  
The Impact

Abstract The Diyab play is an emerging unconventional play in the Middle East. Up to date, reservoir characterization assessments have proved adequate productivity of the play in the United Arab Emirates (UAE). In this paper, an advanced simulation and modeling workflow is presented, which was applied on selected wells located on an appraisal area, by integrating geological, geomechanical, and hydraulic fracturing data. Results will be used to optimize future well landing points, well spacing and completion designs, allowing to enhance the Stimulated Rock Volume (SRV) and its consequent production. A 3D static model was built, by propagating across the appraisal area, all subsurface static properties from core-calibrated petrophysical and geomechanical logs which originate from vertical pilot wells. In addition, a Discrete Fracture Network (DFN) derived from numerous image logs was imported in the model. Afterwards, completion data from one multi-stage hydraulically fracked horizontal well was integrated into the sector model. Simulations of hydraulic fracturing were performed and the sector model was calibrated to the real hydraulic fracturing data. Different scenarios for the fracture height were tested considering uncertainties related to the fracture barriers. This has allowed for a better understanding of the fracture propagation and SRV creation in the reservoir at the main target. In the last step, production resulting from the SRV was simulated and calibrated to the field data. In the end, the calibrated parameters were applied to the newly drilled nearby horizontal wells in the same area, while they were hydraulically fractured with different completion designs and the simulated SRVs of the new wells were then compared with the one calculated on the previous well. Applying a fully-integrated geology, geomechanics, completion and production workflow has helped us to understand the impact of geology, natural fractures, rock mechanical properties and stress regimes in the SRV geometry for the unconventional Diyab play. This work also highlights the importance of data acquisition, reservoir characterization and of SRV simulation calibration processes. This fully integrated workflow will allow for an optimized completion strategy, well landing and spacing for the future horizontal wells. A fully multi-disciplinary simulation workflow was applied to the Diyab unconventional play in onshore UAE. This workflow illustrated the most important parameters impacting the SRV creation and production in the Diyab formation for he studied area. Multiple simulation scenarios and calibration runs showed how sensitive the SRV can be to different parameters and how well placement and fracture jobs can be possibly improved to enhance the SRV creation and ultimately the production performance.

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.

All-Electric Intelligent Completion System: Evolution of Smart Completion

2021 ◽  
Author(s):  
Orient Balbir Samuel ◽  
Ashvin Avalani Chandrakant ◽  
Fairus Azwardy Salleh ◽  
Ahsan Jamil ◽  
Zulkifli Ibrahim ◽  
...  
Keyword(s):  
Phase 1 ◽  
Cost Optimization ◽  
Control Valve ◽  
Production Optimization ◽  
Economic Life ◽  
Control Line ◽  
Flow Control Valve ◽  
Efficient System ◽  
Associated Gas ◽  
Field Development

Abstract Field D is a mature offshore field located in East Malaysia. A geologically complex field having multiple-stacked reservoirs with lateral and vertical faulted compartments & uncertainty in reservoir connectivity posed a great challenge to improve recovery from the field. Severe pressure depletion below bubble point and unconstrained production from gas cap had contributed to premature shut-ins of more than 50% of strings. As of Dec 2019, the field has produced at a RF less than 20%. Initial wells design consisted of conventional dual strings & straddle packers with sliding sleeves (SSD). Field development team was challenged for a revamp on completion design to enhance economic life of the depleting field. In 2015, as part of Phase-1 development campaign, nine wells including four water injectors were completed initiating secondary recovery through water flood. An approach of Smart completion comprising of permanent downhole monitoring system (PDHMS) and hydraulic controlled downhole chokes or commonly known as flow control valve (FCV) was adopted in all the wells in order to optimize recovery from the field and step towards intervention-less solutions. Seeing the benefits of intelligent completion in Phase-1, Phase-2, drilled and completed in 2019 – 2020 has been equipped with new technology "All-electric Intelligent Completion System" in 4 out of 8 oil producers. The new design addresses the reservoir complexity, formation pressure and production challenges and substantial cost optimization, phasing out the load of high OPEX to CAPEX. Installation of "All-electric Intelligent Completion System" has proven to be an efficient system compared to hydraulic smart completions system. It requires 50% to 75% less installation time per zone and downhole FCV shifting time is less than five minutes compared to several hours full cycle for hydraulic system. The new system has capability to complete up to 27 zones per well with single cable. It gave more options and flexibility in order to selectively complete more zones compared to hydraulic FCVs which requires individual control line for each zone. Future behind casing opportunities (BCO) have been addressed upfront, saving millions of future investment on rig-less intervention. In addition to that, non-associated gas (NAG) zones have been completed to initiate in-situ gaslift as and when required avoiding the dependency on aging gaslift facility. The scope of the paper is to show case the well design evolution during Field D development and highlight on how smart completion has evolved from original dual completion to hydraulic smart and recently to electric smart system, how it has contributed to cost and production optimization during installation and production life and also support the gradual digitalization of the Field D. In the end it demonstrates the optimized completion design to enhance the overall economic life of the depleting field.

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