January 29, 2012
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№ 1 (January 2011)

Choosing the Optimum Artificial Lift System Methodology

   The aim of this Artificial Lift System Methodology Selection (ALS MS) study is to select the most appropriate artificial lift method according to conditions and limitations in reservoir, well and surface. After gathering the information it was possible to develop representative well computer models to simulate the real wells’ behavior as close as possible.

By Mišo Soleša, CMS Prodex

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   Once the basic well models were built, the other objectives that should be fulfilled are:
To determine which types of artificial lift methods (ALM) are applicable and the most suitable for the lifting of fluids produced in field;
To assure that selected ALM and equipment will be able to maintain optimum drawdown, so the future reservoir management and completion can be conducted efficiently;
To provide the required input for proper completion selection and design;
To assure that the selected Artificial Lift System (ALS) will enable production of desired reservoir fluids at the acceptable rate;
To assure enough flexibility in order to manage some technical uncertainties and mitigate risk (changeable well performance characteristics, equipment reliability factors etc.);
To analyze relative costs and economic benefits of various ALS and determine economic viability including risk assessment of some reservoir and fluid uncertainties.

Introduction

   Correct selection of an artificial-lift method is important to the long term profitability of most producing oil wells. Proper artificial lift method selection is also very important for gas wells that load up with liquid and for coalbed methane wells that must be dewatered. A wrong ALM selection can reduce production and increase operating costs substantially.

   The workflow that could be applied for the selection of the best ALM for the oil and gas wells is a comprehensive step-by-step procedure that features the following activities:
Collecting and reviewing production and pressure histories for currently tested wells:
a.    Analysis of the wells history enabled identification of specific behavior of each particular well.
b.    Pressure survey data, PVT reports, reservoir characteristics, fluid data (laboratory analysis related to various downhole production problems- emulsion, hydrate, organic scales, sands and salt deposition) were collected and summarized.
c.    The quality of data was checked and reviewed and used for modeling purposes.
Additional data should be requested to identify subsurface uncertainties impacts on future well performance. As there are  a lot of uncertainties related to future  production profile, the wells can be classified into different classes and for each group of wells the range of productivity indices needs to be defined (base case, minimal and maximum)

   The collected information was included into developed multi-criteria ranking model and assigned to various factors (completion, production, efficiency etc.). The importance of each factor is qualified and used for generating the AL selection matrix. It is allowed to eliminate some ALM with lowest score and to continue the process of selection including technical, economic and risk parameters.

   To finalize the ALM selection process, the integrated (technical/economic and risk) evaluation of preliminary selected methods/systems should be applied.
Fulfilling of the future needs related to well completion and  mitigation of the expected problems (well performance changes, real time monitoring and control, chemical injection, plugging with organic and inorganic scales, unexpected problems with suddenly increased water cut  etc).
The methodology and action workflow is shown on Fig. 1.

Preliminary Artificial Lift Selection

   The preliminary selection of the best ALS for the life of project on oil and gas field can be done by using multi-criteria model (ranking matrix). The most pertinent parameters could be grouped into six general classes. Within general classes (macro level) we have extracted the subclasses of parameters (micro level) with defined importance (evaluation), as shown on Fig. 2.

   By using the evaluations of particular ALM (sucker rod pump, electrical submersible pump, progressive cavity pumps, continuous and intermittent gas lift, etc) and the importance of particular parameters on micro level, the evaluation matrices can be generated.

   The level of importance of a certain parameter alters as a function of final effectiveness of the applied method. If, during the application of selected method(s), it is found out that there is a discrepancy and well production results are not in accordance with predicted behavior, then the extracted parameters have to be reanalyzed, the estimation corrected, and the newly achieved estimation will be used for the next case.

Multi-сriteria Model Structure and Features

The general factors (Fig. 3) required for developing the analytical multi-criteria model on macro-level are:
 Applied completion systems (WCF);
 Well and reservoir production history (QHF);
 Pressure history data (PHF);
 Current well performance (WPF);
 Expected problems (EPF);
 Costs, Efficiency and Logistics (CELF).

These evaluations are the results of the following:
Characteristics of the past and current well performance;
Expected problems;
Knowledge and experience of experts and field personnel;
Natural, technical and economical limitations;
Defined rules depending on applicability of analyzed artificial lift methods;
Advantages and disadvantages of analyzed ALM;
Five-level evaluation can be used in for preliminary selection using MCM  (Table 1).

   The evaluation of zero (0) for certain criteria automatically eliminates the possibility of applying some of the methods. The Level of importance of certain parameter alters as a function of final effectiveness of the applied method. The rank of some parameters can be changed if it is found out that there is a discrepancy between expected and real system behavior and rank corrections is a part of learning process and model modifications. The key element of the model is the general factor classes factor on macro level (GFC).

    (2)

where:
k – stands for general class
n - is number of subclasses
Final evaluation of production method application possibility is equal to geometrical average of Integrated Evaluation Factor (IEF, eq.2 for particular general factor classes. The number of general factor classes is variable so that IEF for each well includes only those parameters for which there is a possibility to be defined.

    (1)

If there is a lack of information about well and if it is not possible to define estimation of some factors, a number of multipliers can be reduced. The influence of the parameters for which there are no data should minimized since their influence has not been taken into account.
Example of ALM selection and final result for preliminary selection is shown in Table 2.

Structure of Economic Model

   Economic evaluation of preliminary selected artificial lift methods and required investment are the major parameters influencing on final recommendation. The detailed economic study should be done with an aim to get economical parameters for various conditions and preliminary selected ALM including various vendors. As results of such analysis it is possible to generate comparative economic plots as reliable tools for making proper decision.

   The key elements of the economic model are shown in Fig. 4.
As shown in the figure, the economic model consists of:
Expected oil/gas price database;
Estimated prices for energy, chemicals, workover, maintenance;
Price list for selected equipment.

   The module for capital expenditure (CAPEX) should be used to calculate the required capital investment for the preliminary selected artificial lift methods.
Results of comparative economic analysis including the revenue from selling the oil only can be presented in terms of the discount cash flow and net present value (NPV).

   For the ALM selection, the net oil production, the ultimate net cash flow and discounted present value of the total net cash flow are usually sufficient to make a right decision and to select the best ALM method according to the reservoir, well and market conditions.

   From the presented plots in Fig. 5 it is obvious that Electrical Submersible Pump (ESP) and Progressive Cavity Pump with a downhole motor (PCPDM) shows the best performance indicators expressed in terms of discount cash flow (DCF).
The difference between ESP and PCPDM is being larger after DCF reached the maximum value (correspond to 3-4 years after production started). It is also related with the water cut changes and the behavior of ESP and PCP under conditions when the stable viscous emulsion could be generated.

   After the discounted net cash flow was generated the NPV of various ALM (Fig. 6) can be determined and using risk analysis the probability distribution for NPV could help in making the final decision, as shown in Fig. 7. The final plots (Fig. 8) show the comparison between the most probably NPV for ESP, PCPDM and CGL. Based on the obtained results of discounted cash flow and risk analysis of NPV, ESP and PCPDM are the most preferred ALM method on this randomly selected field.  

Conclusion

   The preliminary selection of the artificial lift selection needed for detailed technical and economical analysis can be prepared by using multi-criteria model which is generally described in the paper. Using available information (well completion, fluid properties, results of analysis and interpretation, current well performance data) the evaluation matrices should be generated. The expert evaluations used in the model are based on the accumulated experience and published knowledge.

   Once the preliminary ALM is selected, the comprehensive economic evaluation methods for the making final decision and selection the best ALM should be applied. The economic model is slightly modified by using a changeable product (oil/gas) prices, production rates influenced by a well performance, estimated target run life of selected equipment and equipment capabilities of a various vendors.

   The initial step is to make detailed design and to decide what equipment will be required to accomplish the expected production rate. Among the most common economic indicators of economic evaluation, the discounted cash-flow rate of return (DCFR) and net present value (NPV) are used for final ALM selection. In general, no one method is by itself a sufficient basis for judgment. It must be recognized that such a quantified profitability measure should be used in combination with other methods, like risk analysis, to include some unpredictable factors and uncertainties that cannot be accounted for (reservoir/well and equipment uncertainties). Using risk analysis of NPV  in terms of number of wells, well performance/productivity,  costs of equipment and chemicals, manner how to handle the anticipated problems(scale, hydrate, emulsion, corrosion, etc.),  estimated run life of equipment and etc, the most reliable and efficient ALM can be selected.

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