地质学
干酪根
油页岩
烃源岩
转化(遗传学)
构造盆地
多孔性
地球化学
矿物学
地貌学
岩土工程
古生物学
生物化学
基因
化学
作者
Christopher J. Modica,Scott G. Lapierre
出处
期刊:AAPG Bulletin
[AAPG/Datapages]
日期:2012-01-01
卷期号:96 (1): 87-108
被引量:274
摘要
Evaluations of porosity relevant to hydrocarbon storage capacity in kerogen-rich mudrocks (i.e., source rocks) have thus far been plagued with ambiguity, in large part because conventional core and petrophysical techniques were not designed for this rock type. The growing recognition of an intraparticle organic nanopore system that is related to thermal maturity is beginning to clarify this ambiguity. This mode of porosity likely evolved with the thermal transformation of labile kerogen and probably neither depends nor interacts (except perhaps chemically) with previously assumed matrix or mineral porosity that is dominated by bound water, and that may be largely irrelevant to hydrocarbon storage capacity in these rocks. To address this newly recognized and important nonmatrix kerogen pore system, that is arguably the dominant hydrocarbon storage and mobility network in these rocks, we introduce a relatively simple kinetic model that describes porosity development within kerogen as a function of thermal maturation. Kerogen porosity development is estimated within the upper Albian Mowry Shale in the Powder River Basin of Wyoming to illustrate the approach. Relevant storage capacity is considered to have evolved with thermal decomposition of organic matter during catagenesis, where we estimate that kerogen porosity does not typically exceed 3% of bulk rock volume. Modeled oil-in-place estimates are comparable to residual oil estimates from pyrolysis data (S1) at lower maturities, but exceed pyrolytic S1 yields at higher maturities. We hypothesize, therefore, that a subsurface kinetic porosity model might represent a means to account for S1 losses at surface conditions and to circumvent difficulties surrounding estimations of expulsion efficiencies that are inherent to more traditional mass balance calculations.
科研通智能强力驱动
Strongly Powered by AbleSci AI