Shelf petroleum system of the Columbus basin, offshore Trinidad, West Indies. I. Source rock, thermal history, and controls on product distribution

2004 ◽  
Vol 21 (1) ◽  
pp. 97-108 ◽  
Author(s):  
Richard G Gibson ◽  
Leon I.P Dzou ◽  
David F Greeley
2017 ◽  
Vol 188 (5) ◽  
pp. 33 ◽  
Author(s):  
Marc Blaizot

Global inventory of shale-oil resources and reserves are far from being complete even in mature basins which have been intensively drilled and produced and in which the main parameters of the regional or local oil-prone source rocks are known. But even in these cases, difficulties still occur for deriving reserves from resources: reaching a plausible recovery factor is actually a complex task because of the lack of production history in many shale-oil ventures. This exercise is in progress in several institutions (EIA, USGS, AAPG) or private oil and gas companies on a basin-by-basin basis in order to estimate the global potential. This analytical method is very useful and accurate but also very time consuming. In the last EIA report in 2013 “only” 95 basins had been surveyed whereas for example, no Middle-East or Caspian basins have been taken into account. In order to accelerate the process and to reach an order of magnitude of worldwide shale-oil reserves, we propose hereafter a method based on the Petroleum System principle as defined by Demaison and Huizinga (Demaison G and Huizinga B. 1991. Genetic classification of Petroleum Systems. AAPG Bulletin 75 (10): 1626–1643) and more precisely on the Petroleum System Yield (PSY) defined as the ratio (at a source-rock drainage area scale) between the accumulated hydrocarbons in conventional traps (HCA) and hydrocarbons generated by the mature parts of the source-rock (HCG). By knowing the initial oil reserves worldwide we can first derive the global HCA and then the HCG. Using a proxy for amount of the migrated oil from the source-rocks to the trap, one can obtain the retained accumulations within the shales and then their reserves by using assumptions about a possible average recovery factor for shale-oil. As a definition of shale-oil or more precisely LTO (light tight oil), we will follow Jarvie (Jarvie D. 2012. Shale resource systems for oil & gas: part 2 – Shale Oil Resources Systems. In: Breyer J, ed. Shale Reservoirs. AAPG, Memoir 97, pp. 89–119) stating that “shale-oil is oil stored in organic rich intervals (the source rock itself) or migrated into juxtaposed organic lean intervals”. According to several institutes or companies, the worldwide initial recoverable oil reserves should reach around 3000 Gbo, taking into account the already produced oil (1000 Gbo) and the “Yet to Find” oil (500 Gbo). Following a review of more than 50 basins within different geodynamical contexts, the world average PSY value is around 5% except for very special Extra Heavy Oils (EHO) belts like the Orinoco or Alberta foreland basins where PSY can reach 50% (!) because large part of the migrated oils have been trapped and preserved and not destroyed by oxidation as it is so often the case. This 50% PSY figure is here considered as a good proxy for the global amount of expelled and migrated oil as compared to the HCG. Confirmation of such figures can also be achieved when studying the ratio of S1 (in-place hydrocarbon) versus S2 (potential hydrocarbons to be produced) of some source rocks in Rock-Eval laboratory measurements. Using 3000 Gbo as worldwide oil reserves and assuming a quite optimistic average recovery factor of 40%, the corresponding HCA is close to 7500 Gbo and HCG (= HCA/PSY) close to 150 000 Gbo. Assuming a 50% expulsion (migration) factor, we obtain that 75 000 Gbo is trapped in source-rocks worldwide which corresponds to the shale-oil resources. To derive the (recoverable) reserves from these resources, one needs to estimate an average recovery factor (RF). Main parameters for determining recovery factors are reasonable values of porosity and saturation which is difficult to obtain in these extremely fine-grained, tight unconventional reservoirs associated with sampling and laboratories technical workflows which vary significantly. However, new logging technologies (NMR) as well as SEM images reveal that the main effective porosity in oil-shales is created, thanks to maturity increase, within the organic matter itself. Accordingly, porosity is increasing with Total Organic Carbon (TOC) and paradoxically with… burial! Moreover, porosity has never been water bearing, is mainly oil-wet and therefore oil saturation is very high, measured and calculated between 75 and 90%. Indirect validation of such high figures can be obtained when looking at the first vertical producing wells in the Bakken LTO before hydraulic fracturing started which show a very low water-cut (between 1 and 4%) up to a cumulative oil production of 300 Kbo. One can therefore assume that the highest RF values of around 10% should be used, as proposed by several researchers. Accordingly, the worldwide un-risked shale-oil reserves should be around 7500 Gbo. However, a high risk factor should be applied to some subsurface pitfalls (basins with mainly dispersed type III kerogen source-rocks or source rocks located in the gas window) and to many surface hurdles caused by human activities (farming, housing, transportation lines, etc…) which can hamper developments of shale-oil production. Assuming that only shale-oil basins in (semi) desert conditions (i.e., mainly parts of Middle East, Kazakstan, West Siberia, North Africa, West China, West Argentina, West USA and Canada, Mexico and Australia) will be developed, a probability factor of 20% can be used. Accordingly, the global shale-oil reserves could reach 1500 Gbo which is half the initial conventional reserves and could therefore double the present conventional oil remaining reserves.


2021 ◽  
pp. 526-531
Author(s):  
Haider A. F. Al-Tarim

The study of petroleum systems by using the PetroMoD 1D software is one of the most prominent ways to reduce risks in the exploration of oil and gas by ensuring the existence of hydrocarbons before drilling.      The petroleum system model was designed for Dima-1 well by inserting several parameters into the software, which included the stratigraphic succession of the formations penetrating the well, the depths of the upper parts of these formations, and the thickness of each formation. In addition, other related parameters were investigated, such as lithology, geological age, periods of sedimentation, periods of erosion or non-deposition, nature of units (source or reservoir rocks), total organic carbon (TOC), hydrogen index (HI) ratio of source rock units, temperature of both surface and formations as they are available, and well-bottom temperature.      Through analyzing the models by the evaluation of the source rock units, the petrophysical properties of reservoir rock units, and thermal gradation with the depth during the geological time, it became possible to clarify the elements and processes of the petroleum system of the field of Dima. It could be stated that Nahr Umr, Zubair, and Sulaiy formations represent the petroleum system elements of Dima-1 well.


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