Seismic Reservoir Characterization

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The petroleum industry is facing a future where new technologies, creativity and integration of different disciplines, are at the core of focus for higher exploration success rates and improved oil recovery. Seismic reservoir characterization plays an essential role in integrated exploration and reservoir studies, as its purpose is the same as that of any reservoir characterization; to provide an optimal understanding of the reservoir’s internal architecture by mapping its properties, such as thickness, net-to-gross ratio, pore fluid, porosity, permeability and water saturation.

Traditionally, reservoir characterization has been done in a field development environment using data from well logs but within the past few years, it has become possible to make some of these maps using seismic. Post-stack and pre-stack seismic data interpretation, inversion tools, seismic attributes and more advanced seismic techniques are being used for both matrix and fracture characterization to add significant value and to allow more wells to be explored, developed and to finally produce their oil and gas reserves successfully.

General Workflow

Reservoir characterization relies on an array of different exploration and production (E&P) data compiled from geoscience studies. Transforming raw data into actionable knowledge is fundamental when devising a strategy and taking the best decisions in the oil and gas industry.

According to Holdaway and Irving[1], the acceptable outcomes are based on a rigorous and statistical analysis of the data and this refers to the fact that the first step in a traditional seismic interpretation workflow produces a large-scale structural perspective of the field of study, in which parameters like reservoir geology, geochemistry, geomechanics, faults, fractures, and stress regimes are key to continue the process of reservoir characterization.

It should be stressed that some methods used in seismic reservoir characterization are purely statistical and others are based on physical models. A group of remarkable authors[2], the optimum strategy is to combine the best of each method to generate results much more powerful than possible from purely statistical or purely deterministic techniques.

Seismic reservoir characterization invariably adopts a suite of workflows or methodologies that incorporate seismic techniques, the main focus being attributes (See Important Papers section to find more information about which attributes are better for reservoir characterization), and the main goal being the characterization of hydrocarbon reservoirs. (Figure to illustrate basic method) A general workflow has the following steps:

  • Data integration and quality control
  • Exploratory data analysis
  • Principal component analysis
  • Self-organizing maps
  • Basin characterization

Both conventional and unconventional reservoirs can be addressed by the data-driven methodology to answer questions on flow assurance, drilling performance, well categorization for field compartmentalization, optimization for additional strategies, and tactics in mature fields as well as reservoir characterization[3].

An overall workflow specifically for unconventional reservoirs was proposed by Leiceaga, Norton, and Calvez[4], demonstrating how seismic-derived elastic properties may be used to help evaluate hydrocarbon production capacity in unconventional plays such as tight or shale formations. First, by combining seismic data with well log data inversion-based volumes of elastic properties may be produced. Next, a petrophysical evaluation and rock physics analysis should be carried out, thus leading to a spatial distribution of hydrocarbon production capacity. And finally, the result obtained is corroborated with the available well information, proving once again that the best workflows for seismic reservoir characterization are the ones that integrate petrophysical, geological and engineering skills into the equation.

Important notes about seismic reservoir characterization

  • It should be noted that quality surface seismic data and proper processing are essential to characterize any reservoir when expecting optimal results, as pointed by research done specifically in Marcellus Shale [5].
  • Regarding oil fields with problems, such as having a very high water cut, despite having high well density reservoirs between wells cannot be clearly defined by known well data. The effect of seismic reservoir characterization was stated as an improvement[6] only by bringing dense well data into full play and through multi-disciplinary approaches.
  • Holdaway and Irving[7] explain how Neural networks are often implemented to map rock properties, calculated from cores and well logs to seismic attributes. In addition, adaptive neuro-fuzzy logic systems and type-2 fuzzy logic systems have shown gradations in the successful application for reservoir characterization.
  • There is always room for improvements and related to the transformation from seismic to reservoir data, because it is often based on interpretations or statistical correlations without accounting for the physical link between seismic wave propagation and reservoir properties, Avseth[8] claims that there is a need to improve the physical understanding of seismic information before using it in reservoir characterization.


References

  1. Knovel, Holdaway, Keith R. Irving, Duncan H. B., 2018, Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models. John Wiley & Sons.
  2. Mukerji, T., Avseth, P., Mavko, G., Takahashi, I., González, E.F., 2001, Statistical rock physics: Combining rock physics, information theory, and geostatistics to reduce uncertainty in seismic reservoir characterization, The Leading Edge, 313-319, [1]
  3. Knovel, Holdaway, Keith R. Irving, Duncan H. B., 2018, Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models. John Wiley & Sons.
  4. Leiceaga, G.G., Norton, M., Calvez, J.L., 2013, An integrated seismic reservoir characterization workflow to predict hydrocarbon production capacity in unconventional plays, [2]
  5. Koesoemadinata, A.,El‐Kaseeh, G., Banik, N., Dai, J., Egan, M., Gonzalez, A., Tamulonis, K., 2011, Seismic reservoir characterization in Marcellus shale [3]
  6. Gan, L., Dai, X., Zhang, X., Li, L., Du, W., Liu, X., Gao, Y., Lu, M., Ma, S., Huanh, Z., 2012, Key technologies for seismic reservoir characterization of high water-cut oilfields, Petroleum Exploration and Development, Volume 39, Issue 3, 391-404 [4]
  7. Knovel, Holdaway, Keith R. Irving, Duncan H. B., 2018, Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models. John Wiley & Sons.
  8. Avseth, P., 2000, Combining Rock Physics and Sedimentology for Seismic Reservoir Characterization of North Sea Turbidite Systems. [5]

See also

Noteworthy Papers