Towards process-based geological reservoir modelling: Obtaining basin-scale constraints from seismic and well data

<p>Basin-scale fluvial architecture is, to a large extent, determined by the ability of river systems to migrate and avulse across their own floodplain. River avulsion takes place when a river aggrades by one channel depth to achieve super-elevation above the surrounding floodplain. However, peat enhancement of floodplain aggradation is likely to affect this fluvial behaviour and has received little attention. The interaction between river channels and peat-dominated floodplains is likely to have the effect of inhibiting or prolonging the conditions required for river avulsion, and so will impact on basin scale architecture during prolonged peat accumulation on floodplains. To elucidate and quantify the nature of this channel-floodplain interaction we investigate the coal-bearing clastic interval of the Carboniferous Pikeville Formation, Central Appalachian Basin, USA. Using a combination of well data and outcrop data, two coal horizons and intervening sand bodies, were mapped across an area of 5700 km<sup>2</sup> to ascertain overall basin-scale architecture. Comparison of the accumulation rate of the coal units (corrected for decompaction) with the synchronously deposited sand bodies suggests that extensive and rapid peat accumulation can increase avulsion timescales by 3 orders of magnitude and dramatically alter basin-scale fluvial architecture.</p>

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A systematic, science-driven approach for predicting subsurface properties

Interpretation ◽

10.1190/int-2019-0019.1 ◽

2020 ◽

Vol 8 (1) ◽

pp. T167-T181

Author(s):

Kelly Kathleen Rose ◽

Jennifer R. Bauer ◽

MacKenzie Mark-Moser

Keyword(s):

Language Processing ◽

A Priori ◽

Basin Scale ◽

Porosity And Permeability ◽

Priori Information ◽

Efficient Exploration ◽

Well Data ◽

Ideal Framework ◽

Discontinuous Data

As human exploration of the subsurface increases, there is a need for better data- and knowledge-driven methods to improve prediction of subsurface properties. Present subsurface predictions often rely upon disparate and limited a priori information. Even regions with concentrated subsurface exploration still face uncertainties that can obstruct safe and efficient exploration of the subsurface. Uncertainty may be reduced, even for areas with little or no subsurface measurements, using methodical, science-driven geologic knowledge and data. We have developed a hybrid spatiotemporal statistical-geologic approach, subsurface trend analysis (STA), that provides improved understanding of subsurface systems. The STA method assumes that the present-day subsurface is not random, but is a product of its history, which is a sum of its systematic processes. With even limited data and geologic knowledge, the STA method can be used to methodically improve prediction of subsurface properties. To demonstrate and validate the improved prediction potential of the STA method, it was applied in an analysis of the northern Gulf of Mexico. This evaluation was prepared using only existing, publicly available well data and geologic literature. Using the STA method, this information was used to predict subsurface trends for in situ pressure, in situ temperature, porosity, and permeability. The results of this STA-based analysis were validated against new reservoir data. STA-driven results were also contrasted with previous studies. Both indicated that STA predictions were an improvement over other methods. Overall, STA results can provide critical information to evaluate and reduce risks, identify and improve areas of scarce or discontinuous data, and provide inputs for multiscale modeling efforts, from reservoir scale to basin scale. Thereby, the STA method offers an ideal framework for guiding future science-based machine learning and natural language processing to optimize subsurface analyses and predictions.

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Reservoir characterisation using process-response simulations: the Lower Cretaceous Rijn Field, West Netherlands Basin

Netherlands Journal of Geosciences – Geologie en Mijnbouw ◽

10.1017/s001677460002014x ◽

2003 ◽

Vol 82 (4) ◽

pp. 313-324

Author(s):

L.J.H. Alberts ◽

C.R. Geel ◽

J.J. Klasen

Keyword(s):

Seismic Data ◽

Lower Cretaceous ◽

Small Scale ◽

Depositional Model ◽

Response Model ◽

Reservoir Properties ◽

Reservoir Heterogeneity ◽

Geological Processes ◽

Basin Scale ◽

Well Data

AbstractPetroleum geologists always need to deal with large gaps in data resolution and coverage during reservoir characterisation. Seismic data show only large geological structures, whereas small-scale structures and reservoir properties can be observed only at well locations. In the area between wells, these properties are often estimated by means of geostatistics. Numerical simulation of sedimentary processes offers an alternative method to predict these properties and can improve the understanding of the controls on reservoir heterogeneity. Although this kind of modelling is widely used on basin scale in exploration geology, its application on field scale in production geology is virtually non-existent. We have assessed whether the recent developments in numerical modelling can also aid petroleum geologists in the interpretation of the reservoir geology.Seismic data, well data and a process-response model for coastal environments were used to characterise the Lower Cretaceous oil-bearing Rijn Field. Interpretation of seismic and well data led to a definition of the structural setting and the depositional model of the Rijn Member in the area. From the sedimentological interpretation the sea-level history could be estimated, which is the one of the most important input parameters for the process-response model.Application of the process-response simulator to the Rijn Field resulted in approval of the depositional model. The output was presented in a 2-dimensional north-south profile, which corresponds very well to the well logs along this section. The results demonstrate that numerical simulations of geological processes can be very useful as a tool to explore many likely geological scenarios. While it cannot be used to supply a unique solution in many cases, it forms a helpful guide during reservoir characterisation to find an optimal scenario of the controls on deposition of the Rijn Member, which contributes to the understanding of the inter-well reservoir heterogeneity.

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