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Predicting Pyrite and Total Organic Carbon from Well Logs for Enhancing Shale Reservoir Interpretation

机译:从井原木预测黄铁矿和总有机碳,以提高页岩储层解释

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Accurate quantification of total organic carbon (TOC) is an important step in evaluating log data in organic-rich reservoirs. The literature describes many log-based approaches for predicting TOC that have been introduced over the years, including the use of uranium content or GR linear regression, bulk density, the DeltaLogR approach, neural network approach, and a response equation-based method using sonic, density, and resistivity logs. All of the approaches require core-to-log calibration for validation. Each of these techniques involves assumptions for them to be valid, and,, in a given instance, it is possible some techniques will not produce reliable results. However, good log-based TOC quantifications can be achieved by taaking the median average of TOC estimates from several indicators. Many shale reservoirs contain 10 wt% pyrite and total organic carbon (TOC), which translates to 7% pyrite and 20% kerogen by volume. High volumetric percentages of pyrite and kerogen significantly affect the rock grain densitty. In low- porosity shale reservoirs, each 0.02 g/cm3 error in grain density produces approximately 1 p.u. error in porosity. Pyrite is commonly present in organic-rich shale intervals of shale gas formations because of the reducing conditions that enhanced organic matter preservation, and it may play a role in decreased resistivity response if the volume is sufficient. Consequently, in shale reservoirs, any method of predicting TOC using resistivity logs, such as DeltaLogR, should also consider the presence of pyrite. Similarly, TOC predictions based on bulk-density logs may also be sensitive to elevated pyrite concentrations. The link between pyrite presence and the depositional environment for many organic-rich shale reservoirs suggests that pyrite and sulfur may be useful TOC indicators in some situations. This paper examines the possible application of pyrite and sulfur for predicting TOC in shale reservoirs, such as in the Haynesville shale reservoir, but results should be applicable to many other shale reservoirs. An interesting result is that, although it may be possible to calibrate a TOC-baased pyrite indicator for individual wells, the calibration is not universally applicablle.
机译:总有机碳(TOC)的精确定量是评估有机储存器中的日志数据的重要步骤。该文献描述了许多基于日志的方法,用于预测多年来引入的TOC,包括使用铀含量或GR线性回归,批量密度,DeltaloGR方法,神经网络方法以及使用Sonic的响应方程的方法,密度和电阻率日志。所有方法都需要核心对数校准进行验证。这些技术中的每一种都涉及它们有效的假设,并且在给定的实例中,可以产生一些技术不会产生可靠的结果。然而,通过从几个指示器的TOC估计的中值平均值来实现良好的基于​​日志的TOC量化。许多页岩储层含有10wt%的硫铁矿和总有机碳(TOC),其转化为7%的黄铁矿和20%的角质剂。高体积百分比的黄铁矿和角质原显然影响岩晶浓度。在低孔隙页岩储层中,晶粒密度的每个0.02g / cm3误差产生约1 p.u。孔隙率误差。由于增强有机物质保存的还原条件,黄铁矿通常存在于页岩气体形成的有机气体形状中,并且如果体积足够,则可能在降低的电阻率响应中发挥作用。因此,在页岩储层中,使用电阻率原木(例如Deltalogr)预测TOC的任何方法也应该考虑硫铁矿的存在。类似地,基于批量密度日志的TOC预测也可以对升高的吡钛石浓度敏感。黄铁矿存在与许多有机富有的页岩储层的沉积环境之间的联系表明,在某些情况下,硫铁矿和硫可能是有用的目标。本文探讨了黄铁矿和硫在页岩储层中预测TOC的可能应用,例如在Haynesville Sheale水库中,但结果应适用于许多其他页岩水库。有趣的结果是,尽管可以为各个井进行校准TOC-BAASED硫铁矿指示器,但校准不是普遍存在的。

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