印度近海三角洲储层正演地层模拟技术成功应用改进储层相模型

A. Moharana, M. Mahapatra, S. Chakraborty, D. Biswal, K. Havelia
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引用次数: 0

摘要

石油地质学家一直是一个研究岩石、发展和描述沉积概念、绘制结构图以发现和开发碳氢化合物以获取利润的群体。随着新技术和计算能力的出现,地质学开始变得更加量化。这场新革命的第一波浪潮是地质统计学和地质建模学科的引入,处理定量统计,如变异图、直方图、随机模型,这些模型可以用来对地质不确定性进行数字和范围计算。然而,最初于1950年代在采矿业中发展起来的地质统计学更多地处理定期抽样的数据,描述它们的空间变异性和方向性。在大多数开发领域,有许多井对储层进行采样,地质统计学可以帮助我们在强大的概念地质基础的支持下,为地下储层创建一个可行的代理。然而,随着井数的减少,用于地质统计分析的数据减少,地质建模人员必须强烈依赖概念地质知识来建立预测地质模型,而不是过度依赖盲目地质统计所能提供的嘈杂图像。直到最近,对于地质学家来说,除了一些方框图和平均砂分布图之外,还没有办法在3D中量化或可视化沉积概念。然而,这些大多是手工的、确定的,任何替代概念的周转时间都很长。地质过程模拟(或GPM,也称为地层正演模拟)是地质学家定量工具集中一个相对较新但仍未充分利用的新工具。这项技术的目的是根据定量确定的物理原理,模拟碎屑沉积物的侵蚀、搬运和沉积过程,以及碳酸盐的生长和再分布(Cross 1990;Tetzlaff & Priddy 2001;Merriam & Davis, 2001)。结果表明,该层序的几何形状和组成受海平面变化、古地理、古气候、构造和沉积物输入变化的影响。在其范围内,GPM类似于详细层序地层学。然而,后者是根据观测和推断(主要来自地震数据)和概念模型发展起来的,这些模型具体说明在某些条件下(如海平面上升和下降,或沉积物输入的变化)应该预期什么样的地层关系。另一方面,GPM仅基于明渠流、水流、波浪和泥沙运动的数值模拟。观察到的地层是对物理系统建模的结果,然后可以进一步用于地质相模型的细化。(Tetzlaff et. al 2014)在目前的研究中,已经尝试了基于地质过程建模(GPM)的B-9油田三维地质模型,由于三角洲砂层的薄层,不可能从地震数据中了解储层的连续性。由于该油田仅有4口井,传统的基于地质统计学的相模型不足以解释储层分布。因此,采用地层正演模拟与多点统计相结合的方法来准确捕捉地下相非均质性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving Reservoir Facies Model by Successful Application of Forward Stratigraphic Modeling Techniques for Offshore Deltaic Reservoir in India
Petroleum Geologists have always been a group who looked at rocks, developed and described depositional concepts, mapping structures to discover and develop hydrocarbons for profit. With the advent of new technologies and computing power, geology started to become a lot more quantitative. The first wave of this new revolution was the introduction of geostatistics and the discipline of geomodelling, dealing with quantitative statistics like variograms, histograms, stochastic models which could be used to put a number and range on the geological uncertainty. However, geostatistics which was originally developed in the mining industry in the 1950's deals more with regularly sampled data, describing their spatial variability and directionality. In majority of development fields, with many wells sampling the reservoir, geostatistics helps us to create a feasible proxy for the subsurface reservoirs, when it is backed by a strong conceptual geological foundation. However, as the number of wells decreases, the data for geostatistical analysis reduces and a geomodeller must rely strongly on the conceptual geological knowledge, to build a predictive geological model rather than the noisy picture which over-reliance on blind geostatistics can provide. Until recently, there was no way of quantifying or visualizing depositional concepts in 3D for a geologist save for few block diagrams and average sand distribution maps. However, these were mostly manual, deterministic with a long turnaround time for any alternate concepts. A relatively recent and still underused addition to the geologist's set of quantitative tools has been geologic process modeling (or GPM, also called stratigraphic forward modeling). This technique aims to model the processes of erosion, transport and deposition of clastic sediments, as well as carbonate growth and redistribution on the basis of quantitative deterministic physical principles (Cross 1990; Tetzlaff & Priddy 2001; Merriam & Davis 2001). The results show the geometry and composition of the stratigraphic sequence as a consequence of sea-level change, paleogeography, paleoclimate, tectonics and variation in sediment input. In its scope, GPM is similar to detailed sequence stratigraphy. However, the latter has been developed on the basis of observations and inferences, mostly from seismic data, and conceptual models that specify what stratigraphic relationships should be expected under certain conditions (such as sea-level rise and fall, or variations in sediment input). GPM on the other hand, is based solely on numeric modeling of open-channel flow, currents, waves, and the movement of sediment. The observed stratigraphy is the result of modeling a physical system which can then be further used for refinement in a geological facies model. (Tetzlaff et. al 2014) In the currents study a 3D geological model for the B-9 field, based on the Geological Process Modeling (GPM) has been attempted Owing to the thin pays in deltaic sands, understanding reservoir continuity from seismic data was not possible. With only 4 wells available in the field, traditional geostatistics based facies models were inadequate in explaining the reservoir distribution. Thus, a combination of Stratigraphic Forward Modeling with Multi Point Statistics is used to accurately capture sub-surface facies heterogeneity.
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