Optimization of Well Placement Under Time-Dependent Uncertainty

2006 ◽  
Vol 9 (02) ◽  
pp. 135-145 ◽  
Author(s):  
Umut Ozdogan ◽  
Roland N. Horne
Keyword(s):  
History Matching ◽  
Net Present Value ◽  
Optimization Technique ◽  
Production Optimization ◽  
Time Dependent ◽  
Hill Climbing ◽  
Process Data ◽  
Well Placement ◽  
Placement Decisions ◽  
Exploration And Development

Summary Well-placement decisions made during the early stages of exploration and development activities have the capability to improve later placement decisions by providing more information (greater certainty). Therefore, recovery and efficient use of information may add value beyond the amount of oil recovered. This study proposes an approach that emphasizes the value of time-dependent information to achieve better decisions in terms of reduced uncertainty and increased probable net present value (NPV). Unlike previous approaches, well-placement optimization is coupled with recursive probabilistic history-matching steps through the use of the pseudohistory concept. The pseudohistory is defined as the probable (future) response of the reservoir that is generated by a probabilistic forecasting model. To test the results of the proposed approach, an example reservoir was investigated with multiple realizations, all of which match the same production history. The results of this study showed that subsequent well-placement decisions can be improved when probabilistic production profiles obtained from the wells, as they are drilled, are incorporated in the optimization scheme.. Introduction Well placement is one of the important decisions made during the exploration and development phase of projects. Most of the time, the large number of possibilities, constraints on computational resources, and the size of the simulation models limit the number of possible scenarios that may be considered. In these cases, optimization algorithms become extremely valuable in searching for the optimum development scenario. Various approaches have been proposed for production optimization. Bittencourt (1994) optimized the scheduling of a field using the polytope algorithm. Beckner and Song (1995) applied the traveling salesman framework on a well-placement problem, using simulated annealing (SA) to find the optimum locations of the wells. Bittencourt and Horne (1997) hybridized genetic algorithms (GA) with the polytope algorithm and tabu search and referred to this hybrid optimization technique as HGA. HGA was observed to improve the economic forecasts and CPU effort during optimization. Pan and Horne (1998) used kriging as a proxy to the reservoir simulator to decrease the number of simulations. Guyaguler et al. (2000) showed that the number of simulations required to optimize the injector well locations decrease when an HGA was coupled with a kriging proxy. Yeten et al. (2002) coupled GA with hill-climbing methods and an artificial neural network (ANN) proxy to optimize the type, location, and trajectory of nonconventional wells. Guyaguler and Horne (2001) assessed the uncertainty of the well-placement results using utility theory together with multiple realizations of the reservoir. All these approaches considered only the information that was available at the beginning of the optimization process. Data that would become available as the reservoir developed in time was not taken into account.

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.

Streamline Tracing and Applications in Dual Porosity Dual Permeability Models

2021 ◽  
Author(s):  
Tsubasa Onishi ◽  
Hongquan Chen ◽  
Jiang Xie ◽  
Shusei Tanaka ◽  
Dongjae Kam ◽  
...  
Keyword(s):  
History Matching ◽  
Net Present Value ◽  
Time Of Flight ◽  
Rate Allocation ◽  
Production Optimization ◽  
Dual Porosity ◽  
Computationally Efficient ◽  
Present Value ◽  
Sweep Efficiency ◽  
Streamline Tracing

Abstract Streamline-based methods have proven to be effective for various subsurface flow and transport modeling problems. However, the applications are limited in dual-porosity and dual-permeability (DPDK) system due to the difficulty in describing interactions between matrix and fracture during streamline tracing. In this work, we present a robust streamline tracing algorithm for DPDK models and apply the new algorithm to rate allocation optimization in a waterflood reservoir. In the proposed method, streamlines are traced in both fracture and matrix domains. The inter-fluxes between fracture and matrix are described by switching streamlines from one domain to another using a probability computed based on the inter-fluxes. The approach is fundamentally similar to the existing streamline tracing technique and can be utilized in streamline-assisted applications, such as flow diagnostics, history matching, and production optimization. The proposed method is benchmarked with a finite-volume based approach where grid-based time-of-flight was obtained by solving the stationary transport equation. We first validated our method using simple examples. Visual time-of-flight comparisons as well as tracer concentration and allocation factors at wells show good agreement. Next, we applied the proposed method to field scale models to demonstrate the robustness. The results show that our method offers reduced numerical artifacts and better represents reservoir heterogeneity and well connectivity with sub-grid resolutions. The proposed method is then used for rate allocation optimization in DPDK models. A streamline-based gradient free algorithm is used to optimize net present value by adjusting both injection and production well rates under operational constraints. The results show that the optimized schedule offers significant improvement in recovery factor, net present value, and sweep efficiency compared to the base scenario using equal rate injection and production. The optimization algorithm is computationally efficient as it requires only a few forward reservoir simulations.

Optimization of Well Placement

1999 ◽  
Vol 122 (2) ◽  
pp. 64-70 ◽  
Author(s):  
Baris Guyaguler ◽  
Roland Horne
Keyword(s):  
Hybrid Algorithm ◽  
Single Point ◽  
Optimization Technique ◽  
Search Space ◽  
Complex Problem ◽  
Production Optimization ◽  
Optimal Placement ◽  
Optimization Approach ◽  
Injection Wells ◽  
Well Placement

Optimal placement of oil, gas or water wells is a complex problem that depends on reservoir and fluid properties, well and surface equipment specifications, as well as economic parameters. An optimization approach that enables the evaluation of all these information is presented. A hybrid of the genetic algorithm (GA) forms the basis of the optimization technique. GA operators such as uniform, single-point, two-point crossover, uniform mutation, elitism, tournament and fitness scaling were used. An additional operator that employs kriging is proposed. The GA was hybridized with the polytope algorithm, which makes use of the trends in the search space. The hybrid algorithm was tested on a set of mathematical functions with different characteristics in order to determine the performance sensitivity to GA operators and hybridization. Simple test cases of oil production optimization on 16×16 simulation grids with known optimum well locations were carried out to verify the hybrid GA results. Next, runs were carried out for a 32×32 problem. The locations of a production and injection well were optimized in the case of three existing producers. Exhaustive runs were carried out for these cases to determine the effects of the operators, hybridization and the population size on the performance of the algorithm for well placement problems. Subsequently, the optimum configuration of two injection wells were determined with two existing producers in the field. It was observed that the hybrid algorithm is able to reduce the required number of simulations substantially over simple GA. [S0195-0738(00)00502-1]

An Improved B-hill Climbing Optimization Technique for Solving the Text Documents Clustering Problem

2020 ◽  
Vol 16 (4) ◽  
pp. 296-306 ◽  
Author(s):  
Laith Mohammad Abualigah ◽  
Essam Said Hanandeh ◽  
Ahamad Tajudin Khader ◽  
Mohammed Abdallh Otair ◽  
Shishir Kumar Shandilya
Keyword(s):  
Optimization Technique ◽  
Document Clustering ◽  
Text Clustering ◽  
Hill Climbing ◽  
Text Documents ◽  
Clustering Problem ◽  
Text Document ◽  
Text Information ◽  
Amount Of Knowledge ◽  
The Hill

Background: Considering the increasing volume of text document information on Internet pages, dealing with such a tremendous amount of knowledge becomes totally complex due to its large size. Text clustering is a common optimization problem used to manage a large amount of text information into a subset of comparable and coherent clusters. Aims: This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster. Methods: The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean techniques. Results: Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem. Conclusion: The performance of the text clustering is useful by adding the β operator to the hill climbing.

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.

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

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.

scholarly journals Improved CRM Model for Inter-Well Connectivity Estimation and Production Optimization: Case Study for Karst Reservoirs

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 816 ◽  
Author(s):  
Daigang Wang ◽  
Yong Li ◽  
Jing Zhang ◽  
Chenji Wei ◽  
Yuwei Jiao ◽  
...  
Keyword(s):  
Time Delay ◽  
Net Present Value ◽  
Water Injection ◽  
Injection Rate ◽  
Production Optimization ◽  
Carbonate Reservoirs ◽  
Karst Reservoir ◽  
Control Step ◽  
Multi Phase Flow

Due to the coexistence of multiple types of reservoir bodies and widely distributed aquifer support in karst carbonate reservoirs, it remains a great challenge to understand the reservoir flow dynamics based on traditional capacitance–resistance (CRM) models and Darcy’s percolation theory. To solve this issue, an improved injector–producer-pair-based CRM model coupling the effect of active aquifer support was first developed and combined with the newly-developed Stochastic Simplex Approximate Gradient (StoSAG) optimization algorithm for accurate inter-well connectivity estimation in a waterflood operation. The improved CRM–StoSAG workflow was further applied for real-time production optimization to find the optimal water injection rate at each control step by maximizing the net present value of production. The case study conducted for a typical karst reservoir indicated that the proposed workflow can provide good insight into complex multi-phase flow behaviors in karst carbonate reservoirs. Low connectivity coefficient and time delay constant most likely refer to active aquifer support through a high-permeable flow channel. Moreover, the injector–producer pair may be interconnected by complex fissure zones when both the connectivity coefficient and time delay constant are relatively large.

scholarly journals End-Point Model for Optimization of Multilateral Well Placement in Hydrocarbon Field Developments

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3926
Author(s):  
Damian Janiga ◽  
Daniel Podsobiński ◽  
Paweł Wojnarowski ◽  
Jerzy Stopa
Keyword(s):  
Production Rate ◽  
Net Present Value ◽  
Capital Expenditure ◽  
Reservoir Management ◽  
Oil Production ◽  
Management Plan ◽  
Water Production ◽  
Reservoir Water ◽  
Well Placement ◽  
End Point

Drilling cost is one of the most critical aspects in the reservoir management plan. Costs may exceed a million dollars; thus, optimal designing of the well trajectory in the reservoir and completion are essential. The implementation of a multilateral well (MLW) in reservoir management has great potential to optimize oil production. The object of this study is to develop an integrated workflow of end-point multilateral well placement optimization integrated with the reservoir simulator supported by artificial intelligence (AI) methods. The paper covers various types of MLW construction, including: horizontal, bi-, tri-, and quad-lateral wells. For quad-lateral wells, the capital expenditure is highest; nevertheless, acceleration of oil production affects the project’s NPV (net present value), indicating the type of well that is most promising to implement in the reservoir. Tri- and quad-lateral wells can operate for 12.1 and 9.8 years with a constant production rate. The decreasing hydrocarbon production rate is affected by reservoir pressure and the reservoir water production rate. Other well design patterns can accelerate water production. The well’s optimal trajectory was evaluated by the genetic algorithm (GA) and particle swarm optimization (PSO). The major difference between the GA and PSO optimization runs is visible with respect to water production and is related to the modification of one well branch trajectory in a reservoir. The proposed methodology has the advantage of easy implementation in a closed-loop optimization system coupled with numerical reservoir simulation. The paper covers the solution background, an implementation example, and the model limitations.

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