Schenk, O., Spahic, D., Bird, K.J., and Peters, K.E.
Basin and petroleum system modeling is an indispensable tool used to examine the dynamics of sedimentary basins and their associated fluids, in order to evaluate if past conditions were suitable enough for the generation of hydrocarbons, which ultimately could have been preserved there. Basically, modeling helps to reduce hydrocarbon exploration risk. This technology uses deterministic computations to forward simulate (i.e., past to present) the thermal history of a basin and the associated generation, migration, and accumulation of hydrocarbons (Hantschel and Kauerauf, 2009; Peters, 2009). Schlumberger’s advanced basin and petroleum system modeling software PetroMod*predicts the extent and timing of hydrocarbon generation from source rocks, reconstructs the basin architecture, migration pathways, locations of potential traps and accumulations, and evaluates the risk based on various geologic, geochemical, or fluid-flow assumptions (Peters et al., 2009).
Simulations of petroleum-geologic processes through time can be computed in 1D, 2D and 3D to evaluate the geohistory of a well or pseudo-well (1D), a cross section (2D) and/or multi-layered maps (3D). These computations require a conceptual model of the basin history (e.g., deposition and erosion of the strata). Data availability determines the complexity of the conceptual models, e.g., in poorly explored areas a simple sketch of basin stratigraphy and architecture in two or three dimensions (2D or 3D) is recommended as a starting point. A 2D model based on a geologic type section or a 3D model based on subsurface maps from seismic data might be constructed to capture a geologic interpretation of the study area. Even in frontier areas where source rocks have not been identified, basin and petroleum system modeling is a powerful predictive tool, because of the easy flexibility it provides in examining multiple hypotheses of source rock richness and thickness and the volumes and compositions of hydrocarbons that might have been generated (Peters et al., 2009).
Basin and petroleum-system modeling is an iterative process with many interrelated steps, consisting of two main stages: model building and forward modeling (Fig. 1; see Al-Hajeri et al. (2009) and Peters et al. (2009) for detailed description of the workflow).
By presenting the calibrated Alaska North Slope 3D petroleum system model, the main aim of this paper is to evaluate the applicability of the same simulation technology on diverse Russian basins. Similar to northern Alaska, the Siberia petroleum provinces are characterized by some regions that are largely explored and developed, whereas other areas are still in the early exploration phase and so far have not been investigated in detail. As the largest oil producing province, the West Siberian basin supplies approximately 70 percent of the oil and 90 percent of the gas produced in the Russian Federation (EIA, 1997). However, huge amounts of undiscovered resources likely remain, and therefore detailed modeling will be essential to understand the key uncertainties of the petroleum systems and to explore more efficiently. This is briefly discussed with respect to the existing Alaska North Slope 3D petroleum system model, which reconstructs, quantifies, and evaluates the individual petroleum systems, burial history, thermal evolution, migration, accumulation, and preservation of hydrocarbons (Schenk et al., 2011). The results of this geological framework provide an assessment of the remaining potential hydrocarbon resources in this remote, but prolific province. Based on this modeling experience we suggest applying the same modeling principles to Siberian basins located at the central and western part of Siberian platform. This will i) increase understanding of existing and hypothetical petroleum systems, ii) help to assess the remaining potential hydrocarbon resources on a regional scale, iii) help to reduce hydrocarbon exploration risk, and iv) provide a consistent approach to compare and evaluate prospects using one of the most recent and important developments, that is local grid refinement.
East and West Siberian Petroleum Provinces – Geological Complexity and Traditional Exploration
Sedimentary basins across Russian Federation are characterized by several large hydrocarbon provinces containing between 2269 and 2325 known oil and gas fields (Kalamkarov, 2003) (Fig. 2), of which the West Siberian and the East Siberian basins are the most prominent. These two hydrocarbon provinces are characterized by different basin evolutions but both contain large volumes of hydrocarbons. The East Siberian province is a Meso-Neoproterozoic Riphean sedimentary basin (~1650–650 Ma) representing one of the oldest productive hydrocarbon provinces in the world (e.g., Everett, 2010). A recent estimate of the hydrocarbon productive potential of this basin is 29,953 MMBOE (IHS, 2010). The evolution of this very old and large petroleum province is characterized by several sedimentary megacycles, which resulted in accommodation of thick sedimentary deposits (Frolov et al., 2011). The megacycles are distinguished by unconformities with accompanying erosion. The youngest deposits of each megacycle are carbonaceous sediments. The entire depositional system was subsequently deformed by a Neoproterozoic orogenic event. The province is characterized by multiple source and reservoir rocks. Peters et al. (2007) provide biomarker and isotope evidence for the genetic oil families in East Siberia, three of which originated from different organofacies of Precambrian marine marl source rock. However, direct oil-to-source rock correlations are lacking and more understanding is needed for the effect of the complex tectonodeformation history on type of trap structures (Frolov et al., 2011). Considering such uncertainties, a dynamic basin and petroleum system model can provide important information on the timing of trap formation in relation to the process of hydrocarbon generation, migration, and accumulation.
The West Siberian hydrocarbon province is the largest oil and gas producing basin in Russia. Most discovered hydrocarbon relates to the well understood Mesozoic petroleum systems (Bazhenov-Neocomian; Togur-Tyumen; e.g., Peters et al., 1994; Ulmishek, 2003; Vyssotski et al., 2005). Hydrocarbon-rich Mesozoic deposits accumulated after giant basalt emplacements at about the Permian-Triassic boundary. According to Brink (2009) these basalt flows are associated with accreted crust suggesting that the basin’s subsidence cannot be explained by classical stretching models (e.g., McKenzie (1978) and Wernicke (1981) and references cited therein) so that additional analysis and calibration of the basin’s thermal development and subsidence rate are required. Nevertheless, despite such complex geological development, easily assessable hydrocarbon accumulations associated with these major “younger Siberian” prospects are largely explored. Apart from large Cretaceous prospects, the West Siberian basin hosts smaller and thus “less prospective” Jurassic and Paleozoic prospects, which are much less understood (e.g., Karodogin and Nezhdanov, 1988; Ablya et al., 2008), because in addition to the Mesozoic burial, Hercynean deformation, pre-Triassic uplift and erosion as well as early Triassic rifting control these older petroleum systems. By using petroleum system software these so-called “less-prospective” prospects now can be evaluated and quantified in more detail.
Both Siberian provinces show major differences in the exploration strategy, as exemplified by the ratio of number of wells versus seismic data. While in West Siberia the density of wells clustered around hydrocarbon accumulations is very high and only relatively few seismic datasets exist, the situation in East Siberia is reversed. According to the Energy Information Administration Report from 1997 (EIA, 1997) individual reservoir data are available in 70 percent of the fields across the West Siberian province. For both Siberian hydrocarbon provinces, the major initial difficulty is relatively poor integration, management and maintenance of exploration data (such as seismic and well data) that subsequently could lead to an incomplete investigation of petroleum systems and their mechanisms. Consequently, such polarized and non-integrative exploration resulted in production oscillations (as described within the same Energy report). West Siberian intensive production was interrupted by the two phases of oil production decline (8.5 to 4.1 million barrels per day) between 1988 and 1994 and a slight decline from 22.6 to 21.9 trillion cubic feet per year (61.9 to 60.0 billion cubic feet per day) of gas between 1991 and 1993. Phases of production decline raised concerns about the potential of the basin as a long term supplier of oil and gas. However, the sum of the estimated ultimate recovery (EUR) from discovered (EIA) and undiscovered resources (Ulmishek 2003) indicate that significant potential remains in the West Siberian Basin.
In order to better understand key uncertainties of Siberian hydrocarbon systems, we illustrate this advanced prediction technology by presenting the Alaskan North Slope model computed entirely in PetroMod – Schlumberger’s petroleum system modeling software.
Case study – Alaskan North Slope
The Alaska North Slope, including the adjacent Beaufort and Chukchi continental shelves, is one of the remaining petroleum exploration frontiers, and is estimated to contain most of the undiscovered oil and gas resources in the North American Circum-Arctic (Gautier et al., 2009). We presented a calibrated 3D model of the Alaska North Slope region that was constructed and analyzed in collaboration with the U.S. Geological Survey. This study reconstructs, quantifies, and evaluates the development of the individual petroleum systems, burial history, and thermal evolution, as well as migration, accumulation, and preservation of hydrocarbons.
The geologically complex Northern Alaska petroleum province evolved through the tectonic stages of passive margin, rift, foreland basin, and foreland fold and thrust belt. Petroleum was generated from several source rock units, and many reservoirs show evidence of mixing of hydrocarbon source types. Rift-related structures and a regional break-up unconformity are critical trapping and migration components of the largest oil and gas accumulations. In addition, stratigraphic traps that developed during extensional and compressional tectonic regimes show significant resource potential in Jurassic through Cenozoic shelf and turbidite sequences.
Regional modeling of the tectonic and sedimentologic evolution of Northern Alaska through time provides an opportunity to integrate and analyze many aspects of petroleum system development. The model encompasses 275,000 m2 (832 x 520 km with a grid spacing of 1 km; Fig. 3) and includes the Chukchi platform, the Beaufort continental shelf, and the foothills of the Brooks Range. The model is based on 48,000 km of newly interpreted 2D seismic and a database of 400 wells that include calibration and geochemical data. Particular attention was paid to mapping onlap and truncation relations developed during passive margin and rifting stages (Mississippian to Early Cretaceous) in recognition of
their importance as hydrocarbon migration pathways
The overlying Brookian Sequence with a total thickness of up to 8,000 m was deposited during Cretaceous and Cenozoic time in a foreland basin filled by longitudinal progradation from WSW to ENE (Bird, 2001). The reconstruction of this paleo-geometry—diachronous deposition, facies variation, and thickness distribution as well as variations in paleo-basin geometry—was one key element of this study. These time-transgressive deposits were reconstructed by using timelines rather than formations. They were mapped from surface traces and shelf edges. The effects of multiple Tertiary erosion events were also taken into account.
Contour maps of original TOC (TOCo) and HI (HIo) for source rocks were taken from Peters et al. (2006) and extrapolated to the limits of the present study. Thermally immature source rock samples were analyzed using the new ‘Phase Kinetics’ procedure developed and calibrated for PVT-controlled prediction of hydrocarbon phases and properties, such as API and GOR (di Primio and Horsfield, 2006). The results of the analysis were assigned to the respective source rocks.
Abundant well data for the Alaska North Slope allowed calibration of both pressure and temperature in the subsurface. The pressure was calibrated in two steps (rock compressibility and permeability). Heat flow was calibrated against vitrinite reflectance and later cross-checked with corrected bottom-hole temperature data.
Forward deterministic computations were applied to simulate the burial history of the rock units and the generation-migration-accumulation of petroleum within a 3D cube through time (e.g., Hantschel and Kauerauf, 2009). A key aspect of the 3D Alaska North Slope model is that it incorporates the time-transgressive deposition of the Cretaceous-Tertiary Brookian Sequence, the thickness difference between the foothill region and the Barrow Arch, and the diachronous pulses of Tertiary uplift and erosion (Fig. 4).
The model results indicate that the thermal maturity of Pre-Brookian deposits were controlled mainly by progradation of the Brookian Sequence (Fig. 5). The time-transgressive deposition of the Brookian Sequence in combination with overall basin geometry also controls hydrocarbon generation and the direction of migration.
Most migration pathways were directed toward the north with hydrocarbons accumulating mainly in combination structural-stratigraphic traps along the Barrow Arch, such as Prudhoe Bay field. At this super-giant accumulation, North America’s largest, trap formation on the rift shoulder preceded expulsion, resulting in a major accumulation. Biomarkers show that Prudhoe Bay oil is a mixture of oils derived from the Triassic Shublik Formation and Cretaceous Hue Shale with lesser input from the Jurassic Kingak Shale (Peters et al., 2008). These results are consistent with the 3D model (Fig. 6): the Shublik and Kingak source rocks started to expel hydrocarbons during the Cretaceous, mainly in the foreland basin, which migrated northward to the rift shoulder. During Tertiary time burial was mainly restricted to the easternmost parts of the foreland basin and the passive margin north of the rift shoulder where associated tilting and subsidence resulted in hydrocarbon generation from the Hue Shale. These hydrocarbons were expelled downward into a zone related to the Lower Cretaceous (break-up) Unconformity (LCU) along which they migrated toward the Barrow Arch, resulting in late-stage contribution of Hue oil in the Prudhoe Bay field.
Debate persists over the reasons for failure of the Mukluk wildcat well. At the time of drilling, the Mukluk prospect was estimated to contain 1.5 billion bbl of recoverable oil in a Prudhoe Bay look-alike structural-stratigraphic trap, although subsurface imaging was uncertain due to difficulty in assessing seismic velocities through permafrost. Drill cuttings and core data showed extensive oil stain in the target formation. It was referred to at the time as the most expensive dry hole in the world. The Mukluk rocks indicated that oil had once been in the structure, but had migrated away. A crucial element or process of the petroleum system was missing. The 3D model shows that initially petroleum accumulated, but later spilled to the southeast toward the Kuparuk River field through a thin sandstone overlying the break-up unconformity during Tertiary tilting. This example at Mukluk indicates the strength of the technology of basin and petroleum system modeling in predicting generation, migration and accumulation, but also remigration and losses from potential structures.
The 3D petroleum system modeling study of the Alaska North Slope represents one of the largest regional-scale computer models of a sedimentary basin to date and is unique with respect to complexity and details. It provides excellent opportunities for analysis of both regional and local geologic features using ‘local grid refinement’ as indicated by the modeling results of the Mukluk failure. Finally, it provides a training set that shows how to build a regional model of a very complex sedimentary basin. Data availability ranged from regions of early exploration to regions of field development at the prospect scale (see Fig. 3).
The Alaskan model shows how petroleum system modeling can be used to understand and evaluate the impact of numerous geological uncertainties, to minimize exploration risk, and to evaluate regions where focused, future investigation should be conducted. Furthermore, this successful modeling practice emphasizes applications to the complex Siberian petroleum province. The dynamic petroleum system modeling workflow is the optimal solution that provides inexpensive integration of input datasets (geological, geophysical, petrophysical, geochemical), allowing subsequent quantification and prediction of the extent and timing of petroleum systems. In fact, there are attempts to reconstruct East Siberian basin development through geological history. For example, Everett (2010) applied one-dimensional modeling of a pseudo-well from Kovyktinskoe Field to test the possibility of in-place source rock position. However, for more demanding petroleum system modeling and more reliable prognosis of hydrocarbon routes, type, quality and volume of accumulations, already interpreted 2D seismic lines, 3D seismic cubes, geochemical and well data need to be integrated within the PetroMod software and subsequently used as input data to create dynamic models in 2D and/or 3D environments. The choice of 2D or 3D depends on available data and particular exploration and/or production interest. The modeling can be applied at a single well or throughout an entire hydrocarbon province.
The Alaskan case study shows how a calibrated PetroMod dynamic model helps to (i) more accurately reconstruct the regional source rock burials, (ii) constrain regional temperature and pressure conditions including maturity predictions, (iii) determine migration paths and distances or to evaluate the quality of trap seal and (iv) determine of volume of trapped hydrocarbons. Such calculations in the software will reduce risk and help to evaluate the impact of well-known uncertainties associated with the Siberian petroleum system: hydrocarbon maturity variations, migration paths, reservoir rock distribution, in-reservoir alteration (e.g., Gratzer et al., 2011), seal quality, the exact prognosis of deposition chronology, and/or biodegradation level (Everett, 2010).
The Siberian PetroMod dynamic model consequently provides time – cost effectiveness, allowing much better commercial viability of exploration. Most importantly, a calibrated 3D Siberian model will assure more accurate assessments of future well locations across the province and will save exploration costs. Such a regional study can easily be managed by combining all capabilities of 1D, 2D, and 3D modeling in one project. The project contains all exploration data, which can easily be updated by integration of new well and seismic data and their interpretation; i.e., PetroMod is not only a dedicated modeling software, but also an indispensable tool for effective exploration data management on a regional scale.