APPLICATION OF ARTIFICIAL INTELLIGENCE IN

Oil and Gas Exploration

APPLICATION OF ARTIFICIAL INTELLIGENCE IN CO2-EOR PROCESS OPTIMIZATION MSc. BSc. Damian Janiga Prof. Jerzy Stopa Assoc. Prof. Paweł Wojnarowski AGH – University of Science and Technology, Poland

ABSTRACT The application of Enhanced Oil Recovery (EOR) techniques is encouraged by the current oil price and the growing of global demand for oil. Among them carbon dioxide (CO2-EOR) is a technology to improve oil production combine with geological sequestration. Optimization method base on the artificial intelligence (AI) can be used as a tool to support decision about well location, well exposure or well control. AI tools mimic intelligent human behaviors and use the acquired knowledge in new situations. CO2 EOR process involves the determination of optimal well placement and control strategy to maximize objective function express project economic value. The problem of well location, perforation interval or even well type in heterogeneous formation is difficult because of roughness and multimodal of the objective function. Well control optimization involves determining the time dependent operating variables like bottom hole pressure or oil and gas rates. An additional difficulty is associated with number of production and injection wells, and CO2 injection time as well. This paper is an attempt to create synergy between AI tools and reservoir simulation to determine optimum CO2-EOR policy. Optimization procedure base on genetic algorithm and particle swarm which belongs to derivative-free algorithms. Well location is supporting by expert system merge available reservoir information. The combining reservoir modeling and AI tool approach used in this study illustrates an improved approach to optimizing CO2EOR process. Keywords: EOR-CO2, optimization, artificial intelligence INTRODUCTION According to BP report, global energy demand could increase by 34% in years 20162035, with a high fraction of fossil fuels in overall energy balance [1]. Due to lack of new discoveries of large hydrocarbon fields, there is necessity of enhance production from current producing fields. Efficiency increase can be done through the use of advanced measurement techniques, modern drilling technologies and field developing with integrated management system operations. Numbers of scientific papers in last years indicates significance of problem. Developing the optimal production strategy is difficult issue because of wide range of factors and design assumption, which include wells location, perforation interval, type of production or injection wells and well control strategy. In addition reservoir, fluid parameters or its interaction implicit to chosen proper production policy. Interference of these factors makes difficult to predict

16th International Multidisciplinary Scientific GeoConference SGEM 2016

the effect and scale of operations. The impact of individual elements analysis is usually made on the basis of scenario analysis. This approach does not guarantee the optimal solution. The different production scenarios tests are usually manual iterative process, takes into account the main design assumptions based on expert knowledge or experience. The use of the optimization methods as a tool to decision making support has a lot of potential that can be utilized for maximize the technical or economical indicators. Because of complexity of optimization problem it seems obvious to support optimization methods using artificial intelligence. These methods are self awareness allowing mimic intelligent human behaviour, being able to use acquired knowledge in new and problematic situations. Especially interesting seems to be the use of simultaneous modelling and reservoir optimization based on the elements of artificial intelligence enabling effective management of hydrocarbon field since its discovery. OPTIMIZATION INTELIGENCE

OF

CO2

–

EOR

PROCES

USING

ARTIFICIAL

Artificial intelligence (AI) is a set of analytical tools to enable imitation of intelligent human behaviour in solving engineering problems [2,3]. Artificial intelligence methods are present in the oil industry since 1970, and its applications has been widely presented in the world literature using the use of artificial neural networks [4,5], fuzzy logic [6,7], evolutionary computation [8,9]. AI methods are the object of interest in the oil industry, mainly because of the possibility of using some of its elements to the production management and optimization. The enhanced oil recovery methods (EOR) could increase the recovery factor of the geological resources by acting of injected fluid. Among the currently used methods, injection of carbon dioxide is most promising methods. The efficiency of the process is influenced by suitable location of wells that provide injection zones with high oil saturation. The critical point of the management is the selection of appropriate strategies for setting out the necessary volume of injected gas and control policy. Injection of carbon dioxide is classified as third methods of oil recovery [10]. EOR methods deliver energy to support the process of extraction, which is a substitute or a complement to the natural processes occurring in the reservoir. In addition, the EOR methods impact on the physical parameters of reservoir fluids causing the formation of favourable conditions for fluid flow in porous media [11]. EOR methods are successfully used in good recognized fields. Based on the experiences of American oil industry, EOR methods causes increasing of the recoverable reserves which is often more important than the search for new reservoirs, due to the risk of failure. Carbon dioxide injected into the reservoir causes the oil displacement from the pores of the rock. This is accompanied by a number of mechanisms associated with the behaviour of a mixture of oil and carbon dioxide. The main of them are: 

reduce the viscosity and density of oil,



solubility of CO2 in crude oil,



evaporation light fraction,



reducing the surface tension.


The effectiveness of the method is estimated at 5-20 % increase of the recovery factor. Theoretically, all the oil contacting with carbon dioxide can be extracted. The limitation is the heterogeneity of the reservoir and the unfavorable mobility factor causing the viscous fingering. The unstable front displacement causes immobilization of oil, rapid CO2 withdraw in production wells and mobilization of reservoir water. In addition, the miscible conditions of carbon dioxide - oil system, reduces the capillary forces which also may increase oil recovery factor .The most important factors which causes the production lowering are lack of flow control in reservoir and non-optimal amount of pumped gas. The carbon dioxide injection may be the most appropriate method to increase production from selected Polish matured oil fields assuming optimal process operation. Optimization model is based on the elements of artificial intelligence and can be presented in the form of functional dependent on vector of state containing pressure and saturation and control vector (eq.1) yM 

max

[ x M ( t ), u M ( t )]

J ( x M (t ), u M (t )) dt

(1)

Where: y – value of objective function J in M iteration x – reservoir vector of state, u – control vector t – appropriate time step in optimization horizon. However, that for any optimization problem additional assumptions and limitations has occurred. Additional technical, technological and economic limits causes tighten of the problem space to the set of feasible solutions. Constraints should be based on expert knowledge and good industrial practices. Objective function is represented by the discounted cash flows. The presented approach allows the integration of the economic and engineering aspect. The economic value of the project implemented in presented model consists of the revenue from the sale of hydrocarbons, the costs associated with the production and injection, the costs of the reservoir providing and fixed costs. The construction of the control vector in the proposed model allow simultaneous optimization with an analysis of the impact of the location and the type of providing wells, perforations and wells control on the value of the objective function. Simplified diagram (fig.1) of the optimization algorithm consists of several modules coupled to one another on a closed loop iteration. Range of decision variables was estimated on the basis of literature data and experience. The value of the objective function, which will have an economic value will be implemented on the basis of the production profile. The estimated economic value of the project will be transferred to the optimization module with implemented global search algorithms. The algorithm in the next step of action will change the value of decision variables and starts the next iteration.


Fig. 1 Optimization loop.

CASE STUDY The strategy management optimization of CO2-EOR process implemented in this case study assumes the use of 4 production wells and 3 injection wells. Optimization elements will be wells location, perforation intervals and production and injection rates. Calculations were performed using a genetic algorithm. Accordingly, the size of the problem assumed necessity of 1000 iterations of the algorithm. Total injection rate is 150 000 sm3/day. Fig.2 presented numerical model of analyzed fields with average permeability and porosity distribution.

Fig. 2 Numerical reservoir model with average permeability and porosity distribution.

As an optimization results, the best location of production and injection wells was obtained. Wells localizations presented in fig 3 are representing by discrete number corresponding to number of blocks in the numerical model and can be transform into real value.


Fig. 3 Optimal placement of production and injection wells.

The optimal production strategy which takes into account the injection of carbon dioxide has changed the basic reservoir parameters. During the 15 years of injection the reservoir pressure rebuilt to the value of 200 bar and 36.74 % of the geological resources has been recovered. High water cut can be problematic because after 15 years of production, water fraction in flow is over 83 %. Optimal production rates and well influence are presented in fig. 4.

Fig. 4 Optimal production rates and, well to well streamlines

EFFICIENCY ANALYSIS OF OPTIMIZATION ALGORITHM

The decision vector components change during the optimization to maximize the objective function. It can be seen that the whole process consists of two stages, the first stage is responsible for extensive space exploration of feasible solutions and second is responsible for the improvement of the found solution. During the optimization presented mechanisms are at least twice as evidenced in fig 5.


Fig. 5 Changing of values in control vector during optimization

With the start of the optimization search of solutions begins in order to identify potential solutions. After approximately 75 iterations, the dominant mechanism is the second one, introducing minor changes in the variable vector to improve the solution. Random disorder (caused by a mutation) causes at about 270 iteration new exploration of the solution space for up to 750 iterations Following the second approximation, the solution is corrected successively until the last iteration of the algorithm. Launch of the second cycle of exploratory testifies to the fact that the previous solution was local extreme. However, the number of iterations performed to improve the solution suggests the large range of attraction of this extreme.

CONCLUSION 

Elements of artificial intelligence can be successfully adapted to the problems of optimal management of hydrocarbon reserves,



Optimization models can be used as an extension of manual methods of searching,



The elements of artificial intelligence (learning ability, abstraction) can be used in new and problematic situations.



Application of proposed optimization algorithm allows to increase the profitability of oil production from matured fields which is particularly important in the case of low oil prices and the relatively high costs associated with the EOR application.

ACKNOWLEDGEMENTS The research leading to these results has received funding from the Polish-Norwegian Research Programme operated by the National Centre for Research and Development


under the Norwegian Financial Mechanism 2009-2014 in the frame of Project Contract No Pol-Nor/235294/99/2014 REFERENCES [1] BP. Bp energy outlook. Tech. rep., BP, 2016 [2] EBERHART, R., SIMPSON, P., AND DOBBINS, R. Computational intelligence PC tools. Academic Press Professional, Inc., 1996 [3] ZURADA, J., MARKS, R., AND ROBINSON, J. Review of computational intelligence: imitating life, 1995. [4] AL-BULUSHI, N., KING, P., BLUNT, M., AND KRAAIJVELD, M. Artificial neural networks workflow and its application in the petroleum industry. Neural Computing and Applications 21, 3 (2012) [5] AL-MUDHAFER, W., AND ALABBAS, M. Application of a hybrid system of genetic algorithm & fuzzy logic as optimization techniques for improving oil recovery in a sandstone reservoir in iraq. In SPE Latin America and Caribbean Petroleum Engineering Conference (2012), Society of Petroleum Engineers. [6] LIAO, R., CHAN, C., HROMEK, J., HUANG, H., AND HE, L. Fuzzy logic control for a petroleum separation process. Engineering Applications of Artificial Intelligence 21, 6 (2008), 835–845 [7] MOHAGHEGH, S. Virtual-intelligence applications in 3—fuzzy logic. Journal of petroleum technology 52, 11 (2000), 82–87

petroleum

engineering:

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[8] GURIA, C., GOLI, K., AND PATHAK, K. Multi-objective optimization of oil well drilling using elitist non-dominated sorting genetic algorithm. Petroleum Science 11, 1 (2014), 97–110. [9] MONYEI, G., ADEWUMI, O., AND OBOLO, O. Oil well characterization and artificial gas lift optimization using neural networks combined with genetic algorithm. Discrete Dynamics in Nature and Society 2014 (2014) [10] SHENG, J. Enhanced oil recovery field case studies. Gulf Professional Publishing, 2013. [11] RYCHLICKI, S., STOPA, J., AND WOJNAROWSKI, P. Mozliwości zwiększenia efektyw-´ nosci wydobycia ropy naftowej ze złóż karpackich˙ . Wydawnictwa AGH, 2010