首页> 外文学位 >Validation of top-down, intelligent reservoir modeling using numerical reservoir simulation.
【24h】

Validation of top-down, intelligent reservoir modeling using numerical reservoir simulation.

机译:使用数值油藏模拟验证自上而下的智能油藏建模。

获取原文
获取原文并翻译 | 示例

摘要

The technique, that is named Top-Down Intelligent Reservoir Modeling, (not to be confused with BP's TDRM history matching technique), integrates traditional reservoir engineering analysis with Artificial Intelligence & Data Mining (AI&DM) technology to generate a full field model. The distinguishing feature of this novel technique is its incredibly low data requirement in order to perform analysis which leads to savings of time and research resources to obtain accurate predictions. It only requires field production rate and some well log data as porosity, thickness, and initial water saturation to start the analysis and provide complete development strategies of the field. Although it can incorporate almost any type and amount of data that is available in the modeling process to increase the accuracy and validity of the developed model.;In this work three different reservoir models with different characteristics and operational conditions have been generated using a commercial simulator and also using the proposed Top Down Modeling Method. The models were built with different PVT-Initial reservoir conditions (saturated or under-saturated), a different number of wells, and different distributions of reservoir characteristics (introducing heterogeneity).;Production rates and well log data, which had been used in the commercial simulator to produce particular models. The same values of data were imported into Top Down Modeling Software (IPDA & IDEA) to develop a new empirical reservoir model in order to validate the capabilities of Top Down Modeling in predicting production issues of an oil reservoir against the commercial simulator.;Investigation and validation of Top Down Modeling's capabilities included identification of the gas cap development within the formation, identification of infill locations by mapping the remaining reserves and prediction of the production performance for the newly drilled wells. Then the results of Top Down Modeling analysis were closer to the commercial simulation models/results.
机译:该技术被称为“自上而下的智能油藏建模”(不要与BP的TDRM历史匹配技术相混淆),将传统的油藏工程分析与人工智能和数据挖掘(AI&DM)技术集成在一起以生成一个完整的油田模型。这项新技术的显着特点是其进行分析所需的数据量极低,从而节省了时间和研究资源,从而获得了准确的预测。它只需要油田生产率和一些测井数据(例如孔隙度,厚度和初始含水饱和度)即可开始分析并提供油田的完整开发策略。虽然它可以合并建模过程中可用的几乎任何类型和数量的数据以提高开发模型的准确性和有效性。但是,在这项工作中,使用商用模拟器已生成了具有不同特征和运行条件的三个不同储层模型。并使用建议的自顶向下建模方法。这些模型是根据不同的PVT初始储层条件(饱和或欠饱和),不同数量的井和不同储层特征分布(引入非均质性)建立的;生产速率和测井数据已被用于商业模拟器来生成特定模型。将相同的数据值导入“自上而下建模”软件(IPDA和IDEA)中,以开发新的经验储层模型,从而验证“自上而下建模”针对商用模拟器预测油藏生产问题的能力。对“自上而下建模”功能的验证包括:识别地层中的气顶发育情况,通过绘制剩余储量图来识别填充位置以及预测新钻井的生产性能。然后,自顶向下建模分析的结果更接近于商业仿真模型/结果。

著录项

  • 作者

    Gomez, Yorgi.;

  • 作者单位

    West Virginia University.;

  • 授予单位 West Virginia University.;
  • 学科 Engineering Geological.;Engineering General.;Engineering Petroleum.
  • 学位 M.S.
  • 年度 2010
  • 页码 68 p.
  • 总页数 68
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号