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

Author(s):  
Patrick Nwafor ◽  
Kelani Bello

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.

2020 ◽  
Vol 72 (12) ◽  
pp. 33-33
Author(s):  
Chris Carpenter

The final afternoon of the 2020 ATCE saw a wide-ranging virtual special session that covered an important but often overlooked facet of the unfolding digitalization revolution. While the rising wave of digital technology usually has been associated with production optimization and cost savings, panelists emphasized that it can also positively influence the global perception of the industry and enhance the lives of its employees. Chaired by Weatherford’s Dimitrios Pirovolou and moderated by John Clegg, J.M. Clegg Ltd., the session, “The Impact of Digital Technologies on Upstream Operations To Improve Stakeholder Perception, Business Models, and Work-Life Balance,” highlighted expertise taken from professionals across the industry. Panelists included petroleum engineering professor Linda Battalora and graduate research assistant Kirt McKenna, both from the Colorado School of Mines; former SPE President Darcy Spady of Carbon Connect International; and Dirk McDermott of Altira Group, an industry-centered venture-capital company. Battalora described the complex ways in which digital technology and the goal of sustainability might interact, highlighting recent SPE and other industry initiatives such as the GAIA Sustainability Program and reviewing the United Nations Sustainable Development Goals (SDGs). McKenna, representing the perspective of the Millennial generation, described the importance of “agile development,” in which the industry uses new techniques not only to improve production but also to manage its employees in a way that heightens engagement while reducing greenhouse-gas emissions. Addressing the fact that greater commitment will be required to remove the “tougher two-thirds” of the world’s hydrocarbons that remain unexploited, Spady explained that digital sophistication will allow heightened productivity for professionals without a sacrifice in quality of life. Finally, McDermott stressed the importance of acknowledging that the industry often has not rewarded shareholders adequately, but pointed to growing digital components of oil and gas portfolios as an encouraging sign. After the initial presentations, Clegg moderated a discussion of questions sourced from the virtual audience. While the questions spanned a range of concerns, three central themes included the pursuit of sustainability, with an emphasis on carbon capture; the shape that future work environments might take; and how digital technologies power industry innovation and thus affect public perception. In addressing the first of these, Battalora identified major projects involving society-wide stakeholder involvement in pursuit of a regenerative “circular economy” model, such as Scotland’s Zero Waste Plan, while McKenna cited the positives of CO2-injection approaches, which he said would involve “partnering with the world” to achieve both economic and sustainability goals. While recognizing the importance of the UN SDGs in providing a global template for sustainability, McDermott said that the industry must address the fact that many investors fear rigid guidelines, which to them can represent limitations for growth or worse.


2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Bailian Chen ◽  
Jianchun Xu

In oil and gas industry, production optimization is a viable technique to maximize the recovery or the net present value (NPV). Robust optimization is one type of production optimization techniques where the geological uncertainty of reservoir is considered. When well operating conditions, e.g., well flow rates settings of inflow control valves and bottom-hole pressures, are the optimization variables, ensemble-based optimization (EnOpt) is the most popular ensemble-based algorithm for the robust life-cycle production optimization. Recently, a superior algorithm, stochastic simplex approximate gradient (StoSAG), was proposed. Fonseca and co-workers (2016, A Stochastic Simplex Approximate Gradient (StoSAG) for Optimization Under Uncertainty, Int. J. Numer. Methods Eng., 109(13), pp. 1756–1776) provided a theoretical argument on the superiority of StoSAG over EnOpt. However, it has not drawn significant attention in the reservoir optimization community. The purpose of this study is to provide a refined theoretical discussion on why StoSAG is generally superior to EnOpt and to provide a reasonable example (Brugge field) where StoSAG generates estimates of optimal well operating conditions that give a life-cycle NPV significantly higher than the NPV obtained from EnOpt.


Processes ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 980
Author(s):  
Gustavo Meirelles ◽  
Bruno Brentan ◽  
Joaquín Izquierdo ◽  
Edevar Luvizotto

Agent-based algorithms, based on the collective behavior of natural social groups, exploit innate swarm intelligence to produce metaheuristic methodologies to explore optimal solutions for diverse processes in systems engineering and other sciences. Especially for complex problems, the processing time, and the chance to achieve a local optimal solution, are drawbacks of these algorithms, and to date, none has proved its superiority. In this paper, an improved swarm optimization technique, named Grand Tour Algorithm (GTA), based on the behavior of a peloton of cyclists, which embodies relevant physical concepts, is introduced and applied to fourteen benchmarking optimization problems to evaluate its performance in comparison to four other popular classical optimization metaheuristic algorithms. These problems are tackled initially, for comparison purposes, with 1000 variables. Then, they are confronted with up to 20,000 variables, a really large number, inspired in the human genome. The obtained results show that GTA clearly outperforms the other algorithms. To strengthen GTA’s value, various sensitivity analyses are performed to verify the minimal influence of the initial parameters on efficiency. It is demonstrated that the GTA fulfils the fundamental requirements of an optimization algorithm such as ease of implementation, speed of convergence, and reliability. Since optimization permeates modeling and simulation, we finally propose that GTA will be appealing for the agent-based community, and of great help for a wide variety of agent-based applications.


2013 ◽  
Vol 816-817 ◽  
pp. 1154-1157
Author(s):  
Xu Yin ◽  
Ai Min Ji

To solve problems that exist in optimal design such as falling into local optimal solution easily and low efficiency in collaborative optimization, a new mix strategy optimization method combined design of experiments (DOE) with gradient optimization (GO) was proposed. In order to reduce the effect on the result of optimization made by the designers decision, DOE for preliminary analysis of the function model was used, and the optimal values obtained in DOE stage was taken as the initial values of design variables in GO stage in the new optimization method. The reducer MDO problem was taken as a example to confirm the global degree, efficiency, and accuracy of the method. The results show the optimization method could not only avoid falling into local solution, but also have an obvious superiority in treating the complex collaborative optimization problems.


2006 ◽  
Vol 9 (02) ◽  
pp. 135-145 ◽  
Author(s):  
Umut Ozdogan ◽  
Roland N. Horne

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.


2017 ◽  
Vol 6 (1) ◽  
pp. 101-122 ◽  
Author(s):  
Tatyana Plaksina ◽  
Eduardo Gildin

Applications of stochastic evolutionary algorithms in engineering are gaining more attention in practical applications in the oil and gas industry. An important factor to consider when implementing stochastic algorithms is its ability to find the global optimum efficiently. In this study the authors formulate, implement, and test a genetic algorithm with strong elitism to solve a critical problem in the upstream oil industry: how to develop economically an unconventional gas asset. This problem involves finding the optimal number of horizontal wells, the number of transverse hydraulic fracture stages along them, and stage half-length. The described problem is inherently discrete or mixed optimization problem for which the authors develop a conceptually new evolutionary integrated framework that addresses all production design questions. They outline the range of applicability of their workflow and provide ample test cases and results. Their rigorous formulation performs well for a given problem statement and finds the optimal solution that is consistent with the industry accepted optimum.


Author(s):  
Williams Toluse ◽  
Victor Okolo ◽  
Amarquaye Martey

ABSTRACT The Federal Government of Nigeria in a bid to promote indigenous companies participation in the oil and gas sector, and to grow the nation’s production capacity passed legislation in 1999 to foster the exploitation of Marginal Oil Fields (MOFs). MOF is one that is considered non – commercial as a result of strategic business development philosophy of the operator, often times large oil companies. Reservoir management is central to the effective exploitation of any hydrocarbon asset; this dependence is heightened for an undeveloped marginal field. There is no ‘one-size fits all’ approach to reservoir management; this paper reviews some techniques adopted by Midwestern Oil and Gas Ltd in the development of the Umusadege marginal field. These techniques fall under three categories: (I) subsurface study (II) well placement and spacing, (III) integrated surface production and optimization, in accordance with regulatory practices.  The previously acquired 3-D seismic data was reprocessed and interpretation of reservoir heterogeneities within the Umusadege field concessionary boundary carried out form the basis of the initial field development plan. To optimize reservoir drainage, the general principles of non-interference well spacing were employed, and advanced well placement technology was deployed to guarantee optimum well placement within the reservoir for effective and efficient drainage. Subsequently, 14 vertical wells and 4 horizontal wells were drilled to effectively optimize recovery from the field. Prior to bringing these wells on-stream, clean-up and Maximum Efficiency Rate (MER) tests were conducted to determine the optimum choke settings, GOR and water cut limits for all wells. An integrated approach encompassing choke sizing, gas and water production management, vessel and line sizing were implemented on the Umusadege field to maintain and optimize recovery. Crude custody transfer measurements and export were enabled by an optimized Group Gathering Facility (GGF).The above techniques combining new technologies, traditional reservoir and production strategies led to the successful development of the Umusadege field; increasing daily oil production from 2,000 bbls/d from the first well re-entry to approximately 30,000 bbls/day over a 7-year period. This case study proves that with the correct implementation of the key elements of reservoir management the value of any hydrocarbon asset can be maximized in a cost effective, safe and environmentally friendly manner.


1999 ◽  
Vol 66 (1) ◽  
pp. 87-94 ◽  
Author(s):  
S. Chucheepsakul ◽  
C. M. Wang ◽  
X. Q. He ◽  
T. Monprapussorn

This paper deals with the double curvature bending of variable arc-length elasticas under two applied moments at fixed support locations. One end of the elastica is held while the other end portion of the elastica may slide freely on a frictionless support at a prescribed distance from the held end. Thus, the variable deformed length of the elastica between the end support and the frictionless support depends on the relative magnitude of the applied moments. To solve this difficult and highly nonlinear problem, two approaches have been used. In the first approach, the elliptic integrals are formulated based on the governing nonlinear equation of the problem. The pertinent equations obtained from applying the boundary conditions are then solved iteratively for solution. In the second approach, the shooting-optimization method is employed in which the set of governing differential equations is numerically integrated using the Runge-Kutta algorithm and the error norm of the terminal boundary conditions is minimized using a direct optimization technique. Both methods furnish almost the same stable and unstable equilibrium solutions. An interesting feature of this kind of bending problem is that the elastica can form a single loop or snap-back bending for some cases of the unstable equilibrium configuration.


2016 ◽  
Vol 2016 ◽  
pp. 1-28 ◽  
Author(s):  
M. H. Md Khir ◽  
Atul Kumar ◽  
Wan Ismail Wan Yusoff

The ambient seismic ground noise has been investigated in several surveys worldwide in the last 10 years to verify the correlation between observed seismic energy anomalies at the surface and the presence of hydrocarbon reserves beneath. This is due to the premise that anomalies provide information about the geology and potential presence of hydrocarbon. However a technology gap manifested in nonoptimal detection of seismic signals of interest is observed. This is due to the fact that available sensors are not designed on the basis of passive seismic signal attributes and mainly in terms of amplitude and bandwidth. This is because of that fact that passive seismic acquisition requires greater instrumentation sensitivity, noise immunity, and bandwidth, with active seismic acquisition, where vibratory or impulsive sources were utilized to receive reflections through geophones. Therefore, in the case of passive seismic acquisition, it is necessary to select the best monitoring equipment for its success or failure. Hence, concerning sensors performance, this paper highlights the technological gap and motivates developing dedicated sensors for optimal solution at lower frequencies. Thus, the improved passive seismic recording helps in oil and gas industry to perform better fracture mapping and identify more appropriate stratigraphy at low frequencies.


1999 ◽  
Vol 122 (2) ◽  
pp. 64-70 ◽  
Author(s):  
Baris Guyaguler ◽  
Roland Horne

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]


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