Approach to Selection of the Optimal Lift Technique in the Vankor Field

Abstract

Artificial lift selection is one of the key decisions to make while starting production of new oil field. Widely used approaches include application range matrixes, decision trees, expert systems based on fuzzy logic. One of disadvantages of all these approaches is a disability to select the most appropriate lift technique; they usually screen out inapplicable techniques and suggest a variety of equally possible solutions. The main purpose of this study was to develop a new approach that will allow selecting optimal lift method based on economical criteria. An approach was tested on Vankor field. The main idea was to compare different artificial lift techniques applied to the same well using NPV criteria. NPV was calculated as a function of well parameters, fluid properties and AL techniques limitations. Rod Pump, ESP, Gas Lift, PCP as well as natural flowing wells are considered. An application called Technologies Application Pattern (TAP) was developed. Well and fluid parameters as long as economical parameters are used as TAP input; the outputs are optimal flowing bottomhole pressure, oil rate and NPV for each technology considered. Each of two production layers of the Vankor field has an aquifer and a gas cap. Natural energy is not enough for ensuring target field production profile and gas and water breakthroughs are main challenges for field development. Horizontal wells with high rates were selected for filed development plan. Based on this decision some of AL techniques were considered inappropriate (rod pump and PCP). The paper discusses main AL selection steps for Vankor oil field. First we consider possible flow rates estimation for each lift technique as functions of GOR, watercut, and productivity index. Based on NPV estimation selection optimal AL is discussed. The final step includes sensitivity and risk analyses. It shows that the solution is more sensible to well depth and GOR. The Monte-Carlo method was used to the risk analysis. Main results of this work are presented in the form of Technologies Application Map – a two-dimension projection of TAP calculation results. Well parameters are set on the axes of the map. Maximum (optimum) NPV for each lift technique is put on the map. Influence of significant parameters (like oil price) uncertainty AL selection is shown.

Subsea Processing Equipment: A Strategy for Effective Assessment and Selection

“…Qualification of some specific components of a particular equipment such as general subsea processing, boosting, Separation and gas compression has also been used in the assessment of these equipment. Only a few of these authors used a quantitative performance measure for reliability predictions of the equipment (Khasanov et al [11], Alemi et al [12], and Rehman et al [13]). Khasanov et al [11] used RAM and FMEA for technology quantification to measure reliability and identify single point failure mode.…”

“…Only a few of these authors used a quantitative performance measure for reliability predictions of the equipment (Khasanov et al [11], Alemi et al [12], and Rehman et al [13]). Khasanov et al [11] used RAM and FMEA for technology quantification to measure reliability and identify single point failure mode. Alemi et al [12] worked on fluid risk analysis and design for operational flexibility to model the steady state and transient performance of the boosting system while Rehman [13] developed a quantitative reliability prediction and performance modeling of multiphase pump system based on ELECTRE.…”

“…Alemi et al [12] worked on fluid risk analysis and design for operational flexibility to model the steady state and transient performance of the boosting system while Rehman [13] developed a quantitative reliability prediction and performance modeling of multiphase pump system based on ELECTRE. The selection techniques that has been used for subsea processing equipment includes: direct comparison of different equipment groups/simulation Khasanov et al [11], artificial intelligence technique (Alemi et al [12], Rehman et al [13], De La Vega et al [14], Chan [15], Ridha [16], Espin et al [17]) and multi-criteria decision making technique with decision matrix (Fatahia et al [18], Park et al [19], Wongnapapisan et al [20]).…”

An Approach for Artificial Lift Applicability Maps Construction (Russian)

“…Предлагаемый метод построения карт применимости является развитием подхода, описанного в работах (Кудряшов, et al 2007) и (Khasanov, et al 2010). Метод основан на использовании узлового анализа для расчета системы «пласт-скважина -скважинное оборудование » (Brown.…”