Ontology-base warehoused time-depth data modelling for improved seismic interpretation in matured onshore producing basins
Authors: Nimmagadda Shastri Lakshman , Dreher Heinz , Rajab Mohammad Reda 
Published: February 2016

Abstract
Time-to-depth conversion has been one of the objectives of seismic interpretation. There is no straightforward procedure that derives a depth section from a seismic section. This is due to the fact that seismic reflection times depend upon the positions and shapes of the reflecting interfaces. The velocity data patterns are often complex, affecting geological interpretations. Depth conversion takes considerable amount of an analyst's time and leaves concerns about its accuracy. This uncertainty in depth conversion procedure significantly contributes to pitfalls and ambiguities in depth conversion velocities.
Ontology based multidimensional modelling has been designed to address these concerns and offers an improved solution in time-depth conversion. More than 150 wells VSP data and ten key horizons are considered for computing time structure maps in onshore basins. This is achieved through analysing seismic-times and sub-sea depths observed at well locations. Interactive QC of "seismic picks" &lt;&lt;&lt; if this is a standard term in the the petroleum geology domain then perhaps write it as 'seismic picks' or offer an explanation via a footnote &gt;&gt;&gt; at sub-sea depths, their velocity data patterns and correlations computed for multiple horizons for several well locations, provide a scope for improving structural anomalies. Seismic times interpreted by horizon tracking at well locations are compared with seismic times interpreted from VSP surveys. These entities and dimensions are documented and integrated in a warehousing environment. Organizing multiple reservoir data of hundreds of well locations in a warehouse environment and matching (and/ or relating) time-depth dimensions at well locations that facilitate computing appropriate depth grids and suiting deriving to geological interpretations, are key highlights of this paper. This modelling procedure minimizes the ambiguity involved in time-depth conversion and imaging for better structural interpretation. Data mining among several variable attributes signify interesting velocity trends that help us to construct linear equations among varying attribute properties. Ontology-base-warehouse modelled time-depth data provide good match between computed depths and actual well tops at well locations, besides interpolating and extrapolating depth values away from wells.

1 Introduction
Time to depth conversion is an important criterion aspect of seismic data interpretation. Velocity plays a key role in interpretation of structures (1), attributed to various geological anomalies. Velocity models are different from field to field in a basin, because of obvious changes in geological attributes. There are many a number of producing wells from multiple horizons from a number of fields in a basin under study. Many wells are producing from the same field for many years, decades and because of which reason, the fields become matured resulting in field maturation. Datasets from these fields need appropriate data integration (2, 3) to extract more oil from matured fields. Arabian Gulf (4) basins are still poorly understood, because of for several reasons. Exploration and production datasets are poorly organized distributed in with different scales and in unmanageable sizes. Geologists and geophysicists explore and build knowledge on sedimentary basins and petroleum systems for to extract more hydrocarbons. Seismic datasets of in these petroleum fields basins possess different scales and datums. Ultimately, seismic times and their corresponding depth data instances are modelled as per the geological data instances, so that the structure of prospect is a true representative of petroleum geology. Time-depth modelling has a key role to play on structural and strati-structural mapping in these matured basins. Understanding of velocity attributes in these basins is still an issue, since many wells produce hydrocarbons from multiple horizons (of differing velocities) located at different depths. Existing historical data can provide clues of for exploring and exploiting many more resources from these basins using the latest information technologies. Ontology-base warehouse modelling (5, 6, 7, 8) can gatherbring and integrate the data from hierarchical and relational data structures, modelled in different domain ontologies.