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Uncertainty Estimation Methodology of Structure Model from Seismic Data in Appraisal Exploration. Case Study, the Field in the Azerbaijan Sector of the Caspian Sea.

机译:评估勘探中地震数据结构模型的不确定性估计方法。案例研究,该领域在里海的阿塞拜疆部门。

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The structure is one of the main parameters that significantly influences the reserves at additional exploration of poorly studied deep horizons of complex deposits. The results of seismic interpretation, the quality of which is affected by each stage of seismic work, are the basis for the structural framework. For deep horizons the seismic error increases, that makes the problem of taking into account the structure variation is relevant. In this article the methodology for estimating structural uncertainty from seismic and well data is described using the example of the unique offshore oil, gas and condensate field in the Azerbaijan sector of the Caspian Sea, which is characterized by an extremely complex tectonic structure. The deposit is associated to a ridge of anticlines of the South Caspian oil and gas basin. The target interval is deep-lying horizons of the underexplored lower interval, penetrated by only a few exploration wells on an area of more than 1000 km2. The pool altitude reaches 1,5 km, which caused a spread in the assessment of reserves of more than 50%. The uncertainty assessment of the structure was based on seismic volumes, VSP and well data. In the process of work, a methodology has been developed that takes into account the errors in data processing, velocity model and interpretation of seismic data. The uncertainty maps are obtained differentially, then combined and taken into account at the stage of constructing the multivariate structural model. To reduce errors in the lower interval, the data of the upper production interval were involved, the structural model of the target horizons was constructed by adding thickness maps to the reference surface taking uncertainties into account. The seismic interpretation of the upper, high-drilled production interval, located above the target at 250-1000 m, and the use of information on production wells in the upper layers, reduced the structural uncertainty for the target horizons of the lower interval by more than 2.5 times. The analysis of uncertainties has shown that the quality of the resulting surfaces is strongly influenced by the velocity model, where the main error comes at the expense of horizons above the target interval. Using upper layers well data, construction of a structural model by adding thicknesses to the reference surface with the transformation depth errors in the thickness errors, made it possible to significantly reduce the error in the velocity model, which in turn reduced the uncertainty of the structure of deep low-drilled horizons and reduced the variance in the amount of reserves under probabilistic estimation. As a result, was developed methodology for estimating the structural uncertainty for poor investigated deep deposits based on the differentially obtained data errors used in the interpretation. The application of this approach in additional exploration of deep reservoirs with a complex tectonic structure has allowed to reduce the spread of reserves with a probabilistic estimate of more than 20%, reliably predict the efficiency of field development and reduce risks in making reservoir management decisions.
机译:该结构是显着影响储备在额外探索的勘探中的额外探索的主要参数之一。地震解释的结果,其质量受到地震作品的每个阶段的影响,是结构框架的基础。对于深度视野,地震误差增加,这使得考虑到结构变化的问题是相关的。在这篇文章中,用于估计从地震和井数据结构不确定性的方法是使用在里海,其特点是一个极其复杂的构造结构的阿塞拜疆区段的唯一的海上石油,天然气和凝析字段的示例说明。该矿床与南部Caspian油气盆地的边缘的脊有关。目标间隔是望远镜的较低间隔的深层视野,仅在超过1000平方公里的面积上仅渗透井。泳池海拔1.5公里,这导致储量评估超过50%。该结构的不确定性评估基于地震体积,VSP和井数据。在工作过程中,已经开发了一种方法,以考虑数据处理,速度模型和地震数据的解释中的错误。在差异上获得不确定性地图,然后在构建多变量结构模型的阶段进行结合并考虑。为了减少较低间隔的误差,涉及上部生产间隔的数据,通过将厚度图添加到参考表面来考虑不确定性来构造目标视线的结构模型。上层,高钻的生产间隔的地震解释,位于目标在250-1000米以上,以及在上层中使用有关生产井的信息,减少了更低间隔的目标视线的结构性不确定性超过2.5次。不确定因素的分析表明,所得表面的质量受速度模型的强烈影响,主要误差以牺牲目标间隔上方的视野为代价。使用上层井数据,通过在厚度误差中向参考表面增加厚度来构造结构模型,使得可以显着降低速度模型中的误差,这反过来又降低了结构的不确定性深度低钻井的视野,降低了概率估计下储备量的差异。因此,基于解释中使用的差异获得的数据误差,开发了用于估计较差的较差的深沉积的结构不确定性的方法。这种方法在具有复杂构造结构的深层水库的额外探索中允许减少储备的储备差,概率估计超过20%,可靠地预测现场开发的效率,降低制造水库管理决策的风险。

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