Integrated characterisation of mud-rich overburden sediment sequences using limited log and seismic data: Application to seal risk

Muds and mudstones are the most abundant sediments in sedimentary basins and can control fluid migration and pressure. In petroleum systems, they can also act as source, reservoir or seal rocks. More recently, the sealing properties of mudstones have been used for nuclear waste storage and geological CO2 sequestration. Despite the growing importance of mudstones, their geological modelling is poorly understood and clear quantitative studies are needed to address 3D lithology and flow properties distribution within these sediments. The key issues in this respect are the high degree of heterogeneity in mudstones and the alteration of lithology and flow properties with time and depth. In addition, there are often very limited field data (log and seismic), with lower quality within these sediments, which makes the common geostatistical modelling practices ineffective. In this study we assess/capture quantitatively the flow-important characteristics of heterogeneous mud-rich sequences based on limited conventional log and post-stack seismic data in a deep offshore West African case study. Additionally, we develop a practical technique of log-seismic integration at the cross-well scale to translate 3D seismic attributes into lithology probabilities. The final products are probabilistic multiattribute transforms at different resolutions which allow prediction of lithologies away from wells while keeping the important sub-seismic stratigraphic and structural flow features. As a key result, we introduced a seismically-driven risk attribute (so-called Seal Risk Factor "SRF") which showed robust correspondence to the lithologies within the seismic volume. High seismic SRFs were often a good approximation for volumes containing a higher percentage of coarser-grained and distorted sediments, and vice versa. We believe that this is the first attempt at quantitative, integrated characterisation of mud-rich overburden sediment sequences using log and seismic data. Its application on modern seismic surveys can save days of processing/mapping time and can reduce exploration risk by basing decisions on seal texture and lithology probabilities

An investigation on the relationships between the petrographic, physical and mechanical characteristics of sandstones from Newspaper Member of the Natal Group using Self Organising Maps

Abstract: Sandstone is the most significant rock type in the Natal Group, and widely extended into the Marianhill Formation. Due to prevalence within the Natal Basin it is widely used as aggregate material therefore it is important to study the physical properties and their relative effect to the mechanical properties. Literature is rather limited, so this study is attempting to investigate the published data, applying the approach of self-organising maps and data-mining techniques in order to extract new knowledge and give further insight into the relationships amongst sandstone petrographic, physical and mechanical characteristics. SOM-based analysis distinguished three clusters, with similar petrographic, physical, geotechnical characteristics, which led to the identification of different lithofacies. Significant parameters dictating cluster identification, are the type of grain contact, void space, packing density, and amount of silica cement

Data mining of petrophysical and lithogeochemical borehole data to elucidate the origin of seismic reflectivity within the Kevitsa Ni-Cu-PGE -bearing intrusion, northern Finland

The Kevitsa mafic-ultramafic intrusion, located within the Central Lapland Greenstone Belt in northern Finland, hosts a large, disseminated Ni-Cu-PGE sulphide deposit. A three-dimensional seismic reflection survey was conducted over the Kevitsa intrusion in 2010 primarily for open-pit mine planning and for deep mineral exploration purposes. In the Kevitsa three-dimensional seismic data, laterally continuous reflections are observed within a constrained region within the intrusion. In earlier studies, it has been suggested that this internal reflectivity mainly originates from contacts between the tops and more sulphide-rich bottoms of smaller scale, internally differentiated magma layers that represent a spectrum of olivine pyroxenites. However, this interpretation is not unequivocally supported by the borehole data. In this study, data mining, namely the Self-Organizing Map analysis, of extensive Kevitsa borehole data is used to investigate the possible causes for the observed internal reflectivity within the Kevitsa intrusion. Modelling of the effect of mineralization and alteration on the reflectivity properties of Kevitsa rock types, based on average modal compositions of the rock types, is presented to support the results of the Self-Organizing Map analysis. Based on the results, we suggest that the seismic reflectivity observed within the Kevitsa intrusion can possibly be attributed to alteration, and may also be linked to the presence of sulphide minerals.Peer reviewe

Predicting Missing Seismic Velocity Values Using Self-Organizing Maps to Aid the Interpretation of Seismic Reflection Data from the Kevitsa Ni-Cu-PGE Deposit in Northern Finland

We use self-organizing map (SOM) analysis to predict missing seismic velocity values from other available borehole data. The site of this study is the Kevitsa Ni-Cu-PGE deposit within the mafic-ultramafic Kevitsa intrusion in northern Finland. The site has been the target of extensive seismic reflection surveys, which have revealed a series of reflections beneath the Kevitsa resource area. The interpretation of these reflections has been complicated by disparate borehole data, particularly because of the scarce amount of available sonic borehole logs and the varying practices in logging of borehole lithologies. SOM is an unsupervised data mining method based on vector quantization. In this study, SOM is used to predict missing seismic velocities from other geophysical, geochemical, geological, and geotechnical data. For test boreholes, for which measured seismic velocity logs are also available, the correlation between actual measured and predicted velocities is strong to moderate, depending on the parameters included in the SOM analysis. Predicted reflectivity logs, based on measured densities and predicted velocities, show that some contacts between olivine pyroxenite/olivine websterite-dominant host rocks of the Kevitsa disseminated sulfide mineralization—and metaperidotite—earlier extensively used “lithology” label that essentially describes various degrees of alteration of different olivine pyroxenite variants—are reflective, and thus, alteration can potentially cause reflectivity within the Kevitsa intrusion

Discriminant Analysis of Kalhur and Pabdeh Formation Members

Lithology identification and petrophysical estimation has always been a challenging task especially when it comes to carbonate reservoirs due to their heterogeneity. Traditionally, inspection of well log data along with core data are the basic source of reservoir characterization. The data available is a single borehole well log data with limited core data of a carbonate reservoir located in Iran. This project demonstrates a simple practical approach to identify the lithology and to estimate the petrophysical properties of an unknown zone from the well log data and limited core data available using statistical analysis