First Break - Volume 35, Issue 5, 2017

This case study presents an integrated sub-surface data case study of a gas discovery cluster at the end of the exploration phase. The objective is to understand what we can do with the data at our disposal to improve the interpretation of the reservoir at Ordovician level, in order to select an optimal location of tree appraisal wells to be drilled in a major discovery during the last exploration phase, and to fine-tune the in-place volumes estimates and associated uncertainties. In this paper, we will highlight three main aspects of the outcomes of the study. First, the methodology applied to generate the inversion, with associated Quality Checks (QC) to transform the seismic signal into sand packages. Secondly, we will highlight some key information extracted from the interpretation that is crucial for the appraisal of the discovery. Finally, we will present how we integrated those results with well information to fine-tune the depositional model of the Upper Ordovician reservoir in the area.

In conventional geological settings, oil companies routinely evaluate prospects for their drilling portfolio where the process of interpreting seismic amplitude anomalies as Direct Hydrocarbon Indicators (DHIs) plays an important role. DHIs are an acoustic response owing to the presence of hydrocarbons and can have a significant impact on prospect risking and determining well locations (Roden et al., 2005; Fahmy 2006; Forrest et al., 2010; Roden et al., 2012; Rudolph and Goulding, 2017). DHI anomalies are caused by changes in rock physics properties (P and S wave velocities, and density) typically of the hydrocarbon-filled reservoir in relation to the encasing rock or the brine portion of the reservoir. Examples of DHIs include bright spots, flat spots, character/phase change at a projected oil or gas/water contact, amplitude conformance to structure, and an appropriate amplitude variation with offset on gathers. Many uncertainties should be considered and analysed in the process of assigning a probability of success and resource estimate range before including a seismic amplitude anomaly prospect in an oil company’s prospect portfolio (Roden et al., 2012).

Interpretation of seismic data is still a process that involves manual work. By applying clustering algorithms to seismic attribute data, it is possible to automate interpretation to a certain degree. The first applications of clustering for seismic interpretation date from the early 1980s (Seber, 1984). In this work Seber applied K-means clustering, which remains one of the main clustering algorithms applied to seismic data. Sabeti and Javaherian (2009) applied K-means clustering to synthetic and real seismic data in an attempt to determine facies changes. Self-organizing maps (SOM) are another important clustering algorithm that has been applied to seismic data (Kohonen, 1990; 2001). Taner (2001) used SOM clustering to subdivide a seismic data set into four lithology classes. Strecker and Uden (2002) and Roy and Marfurt (2010) used SOM–based clustering to describe channel systems. Both Taner (2001) and Strecker (2002) emphasized the importance of well information for calibrating the results. Barnes and Laughlin (2002) applied both K-means and SOM clustering to seismic sections and to a 3D seismic data set. In their work they found a good correlation between the results of K-means and SOM clustering. Although almost no other clustering methods have been applied to seismic data, Paasche and Tronicke (2007) used Fuzzy K-means on 3D GPR data and assumed this method might also be applicable to 3D seismic data. In this work we test various clustering methods for their applicability to the segmentation of seismic data. The project is structured in two phases: firstly on synthetic data and secondly on real seismic. In the first phase we created synthetic structural models of reef bodies, salt structures, channels, karst features, fault zones, and volcanic structures. These models were then forward modelled to generate synthetic seismic data that were used as input for testing of clustering algorithms.

The regional stage of geological exploration in the northern Barents Sea is almost complete. For the seismic observations the density there is more than 0.2 line km/km². The density has been achieved mostly through the surveys by JSC Marine Arctic Geological Expedition performed for the Federal Subsoil Resources Management Agency, Rosnedra, under the Ministry of Natural Resources of the Russian Federation in 2006-2012. The total volume of completed geophysical surveys including 2D CMP reflection seismic acquisition, shipboard gravity and differential marine magnetometer measurements is more than 30,000 line km. The seismic data acquired along with the maps of anomalous potential fields and their continuations allowed for the evaluation of oil and gas potential in that region (Figure 1).

The EarthStudy 360 full-azimuth local angle domain (LAD) imaging and analysis technology, developed by Paradigm, appeared on the geophysical services market in 2012. GazpromNeft NTC, a high-tech company that strives to incorporate the most advanced systems into its operations, did not let this opportunity pass by. A pilot project to test LAD imaging was undertaken in 2014. The study’s most significant findings were anisotropy intensity and direction determinations in terrigenous and carbonate reservoirs, and improved acoustic impedance convergence calculated on a combination of advanced technology results and GIS data. In 2015 GazpromNeft NTC used the technology to assess 3D full-azimuth angle domain survey data in an Eastern Siberian field. Historically, Eastern Siberia has been one of the most difficult areas for seismic studies attempting to prospect for and predict reservoir properties. Complex relief and subsurface velocity heterogeneity in both vertical and lateral directions creates problems for seismic survey operations and for seismic data processing. The greatest challenges occur in depth processing, where specific complexities hinder the development of a depth/velocity model, beginning with near-surface formations and the top portion of the geologic profile. Local lithological variations in the lateral direction, accompanied by the surface exposure of rocks of various ages and lithological characteristics (from carbonates to clays, salt-bearing strata, and sometimes trapped intrusions) make it necessary to develop complex velocity models and apply full-azimuth depth surveys which traditional approaches and migrations, based on the Kirchhoff integral, cannot provide. Cambrian, Jurassic and Quaternary rocks with P-wave velocities of 5000-5500 m/s, 3000-3500 m/s, and 900-1200 m/s, respectively, are exposed at the surface of the survey area.

The ability to determine the relative density and orientations of fractures in potential reservoirs has become increasingly important as resource plays are now a major exploration and development focus for energy companies worldwide. Techniques have been developed using pre-stack data and velocity anisotropy to identify and map fractures. Azimuthal AVO has been employed to estimate fracture density and quantitatively assess how well this approach predicts reservoir quality (Hunt, 2010). Additionally, a new approach to quantitative azimuthal inversion for stress and fracture detection has been developed (Mesdag, 2016). This paper focuses on extracting a relative fracture density attribute and fracture orientations from migrated post-stack 3D seismic volumes. The detection and mapping of fractures in migrated poststack 3D seismic data depends on the resolution and signal-tonoise ratio of the data in the seismic volume. A discussion of resolution problems and the limits of resolution in post-stack 3D seismic data, and structurally-oriented post-stack coherent and random noise filtering is followed by descriptions of a Fracture Density attribute and of the extraction of fracture orientations. An example of the results of applying these processes and workflow is included from the Niobrara shale play in the United States.

Seismic inversion transforms reflectivity data into layer property which adds valuable interpretive benefits for reservoir characterization. Although seismic inversion is routinely conducted in reservoir interpretation projects, there are still pitfalls in using this technology. If not careful, risks exist which may lead to incorrect estimation of reservoir properties and in the worst case to wrong drilling. In this paper, a model-driven inversion is used as an example to devise a practical workflow for typical post-stack inversion projects. The devised workflow consists of (1) log calibration, (2) selection of a seismic trace near a well, (3) well tie analysis, (4) wavelet estimation, (5) seismic amplitude calibration, (6) low frequency model building, (7) inversion job parameterization, (8) volume inversion and (9) interpretation. The technical background of all steps is reviewed and methods of problem solving presented. Emphasis is placed on how to quality control each stage of a multi-step process so that uncertainty is reduced to a minimum. With careful data preparation and job parameterization, model-driven inversion produces both absolute and relative impedances, useful for more accurate stratigraphic interpretation and reservoir property determination.

Colour perception is an intrinsic part of visual cognition and affects how we understand the images that we see. Colour is now a fundamental part of how we display seismic reflectivity and attribute data, but not everyone sees colour in the same way. Colour deficiencies (or colour blindness) alters our perception of an image, and this study investigates whether colour deficiency also alters the interpretation of geological features in seismic data.