This paper addresses proven best practice in modeling workflow including procedure and Qa/Qc criteria, which have to be applied during simulation models construction. The main issues discussed in this paper are as follows:

  • Static model with acceptable petrophysical parameters distribution including and honoring log and model Swi derived data per well as the basis for reliable dynamic model with realistic predictive mode.

  • Dynamic model as management tool with reasonable history match quality as assurance for reliable predictive mode of wells, areas and reservoir performance.

  • Define and quantify the volume of fluids-in-place, movable oil, residual oil and volumetric sweep efficiency to assess the reservoir potential, rate sustainability and economic ultimate recovery.

  • Assess the associated risks to development plans under selected development schemes with water/gas flood, WAG, artificial lift (ESP or Gas lift) and other EOR methods.

  • Model prediction mode quality and impact on strategic development decisions.

As the oil industry has long experience in simulation techniques supported by availability of super computers and advanced software, it is observed that there are still major gaps that are not bridged yet. This paper will highlight some of those gaps and propose effective and practical solution based on best practice and lessons learnt in modeling studies to ensure reliable reservoir simulation predictive mode capabilities.

This paper also includes the main criteria and assurance elements which were used to define modeling procedures that would participate in enhancing model reliability, and how they could impact development optimization process of selected production scheme towards achieving maximum recovery. Summary of these elements is as follows:

  • Static to Dynamic Models Transition Phase

    • Well-per-well Swi match of log and model derived data. Acceptable level and trend match by using representative Pc's based on rock types & petrophysical data, MICP's, Height functions or combination.

    • Stability test to ensure good equilibrium condition with fluids distribution.

    • Well-per-well RFT/MDT field data and model derived data match.

  • Dynamic Model History Match

    • Well-per-well acceptable trend match of observed data can be reached through a cycle of iterative process between geology, static and dynamic models to improve match.

    • Matching parameters and Qa/Qc criteria will be discussed later in details including; oil, gas and water rates and cumulative production, BHCIP, BHFP, WHFP, WCT and GOR.

  • Prediction Mode of Development Plan

    • Well-per-well acceptable trend match (Rate, Pressures, WCT & GOR).

    • In case of abnormal predictive trend, consider the following remedial action:

      1. Review field measured data for accuracy, screen data as justified.

      2. Review imposed model constraints at well, group and field levels.

      3. Investigate solution with iterative process including static and dynamic models based on geology.

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