First Break - Volume 34, Issue 4, 2016

We show the possibility of transforming Airborne EM (AEM) data into music, by means of the simple procedure of data normalization and the application of Musical Instrument Digital Interface (MIDI) routine. For this introductory work, named ‘EMusic’, we exploit the ability of the MIDI protocol to translate numerical values (voltage response) into musical pitches. It is possible to use the large amount of data collected by airborne systems, in order to make easier the comprehension of EM method (for a didactic purpose), to assess quickly the quality of data (for a technical purpose) and, last, but not least, to compose musical pieces (creative purpose). Through preliminary and short samples, we show that it is really possible to achieve a ‘sound’ of a particular geological setting, characterized by a specific musical signature, which could support the data interpretation. It is possible to expand greatly this procedure, also considering other geophysical methods. We point out future steps that could be taken. The idea of transforming scientific data into music (sonification) is not new: as reported by Dell’Aversana (2013), many authors dealt with this topic, mainly by processing seismic data (see further references in the quoted paper). The same author presented music samples extracted from earthquake and volcanic activity, processed through the Musical Instrument Digital Interface (MIDI) protocol. In a second paper, published in 2014, he applied this idea to seismic prospection for detecting gas-filled channels, faults and geological formations, using rhythmic features that reflect the spectral analysis of data. Finally, he suggested that this approach can be applied to any kind of geophysical data and that sonification can complement, not substitute, standard geophysical processing and interpretation routines.

In a sedimentary basin, everything typically present is highly resistive, except brine. A localized region of higher resistivity, whether identified on well logs or from controlled-source electromagnetic (CSEM) data, is therefore indicative of a local reduction in interconnected brine content. This may be due to the presence of fresh water, low-porosity lithologies (including salt, volcanics and some types of carbonate), or hydrocarbons. It is this first-order sensitivity to fluid presence and properties that makes CSEM information of high potential value in an exploration environment (Baltar et al., 2015; Fanavoll et al., 2014; Zweidler et al., 2015). In its simplest form, the use of CSEM for hydrocarbon detection can be considered a two-stage process. First, localized regions of higher resistivity need to be identified from the CSEM data. Second, these ‘anomalous’ regions must be interpreted in terms of their potential for being indicative of hydrocarbon presence. One might reasonably expect that the greater challenge is the latter: successfully predicting the geological cause of an anomalous resistivity. However, in our experience, the initial task of reliably identifying the anomalous features can prove equally challenging without an appropriate process. Early CSEM interpretation workflows, focusing on measurement interpretation, tended to use a ‘threshold normalized amplitude response’ (NAR) rule such as 15% (Hesthammer, 2010). This proved useful in the most simple geologies, but of less value in more complex settings, and also failed to account for relative data quality. Today, the starting-point for CSEM interpretation is subsurface resistivity images. With these, there still exists plenty of leeway for the choice of colour scale to have a large effect on the apparent sizes of any ‘red blobs’ in the study area. We detail here a simple and clear criterion to qualify a feature as anomalous with respect to its surroundings, which is analogous to that followed when qualifying the significance of seismic amplitude anomalies (Roden et al., 2014). We expand on an approach first proposed in Baltar and Roth, 2013, as part of a quantitative interpretation workflow for CSEM, providing a more practical guide to its application and implications. The concept is first illustrated with well data, before the method is detailed with CSEM examples.

In recent years there has been renewed interest in the hydrocarbon potential of the Perth Basin in Western Australia. It is close to the regional capital city and the gas pipeline that runs between Dampier in the north and Bunbury in the south. Recent discoveries of gas by AWE have shown that there is a working hydrocarbon system within at least the northern and central parts of the basin. In most parts of the basin, modern seismic data is relatively scarce. In the current low oil price environment, explorers are looking for cost-effective ways of exploring and targeting seismic acquisition. Airborne gravity gradiometry is such a technique. It has been widely used in frontier basins to understand the basin architecture (Bain et al., 2013, Roberts et al., 2015), sedimentary structure (Feijth et al., 2015 and Kovac et al. 2013) and planning of seismic acquisition (Moore et al., 2012). The Black Swan geophysical survey was conducted by CGG to assist oil and gas producer, Empire Oil and Gas, in identifying target areas for hydrocarbon exploration. The main tools employed were the Falcon Airborne Gravity Gradiometer (AGG), magnetic and digital terrain data. The survey was flown east-west with a nominal flying height of 100 m with 1 000-m line spacing, using a flight line to tie line ratio of 10:1. Structural interpretation has been carried out in two phases. Regional interpretation provided an overview of major regional structures and aimed to analyse the linkage between segments within the exploration blocks. It was derived from regional, publicly available gravity and magnetic data. Detailed structural interpretation was derived from the AGG (Airborne Gravity Gradiometer) data in order to improve current understanding of the tectonic pattern within Empire Oil’s exploration blocks. Depth to magnetic basement was calculated using publicly available government data. The survey identified areas containing large structural leads and trends for targeting future gas exploration activities, including infill 2D seismic acquisition.

One of the major challenges in interpretation of geophysical data remains the ability to jointly invert multiple geophysical datasets for self-consistent 3D earth models of different physical properties. To date, various attempts at 3D joint inversion have been based on either correlations between different physical properties, or by introducing structural similarities. In addition, there could be both physical property and structural correlations between the different earth models, and these complexities cannot be captured by any existing joint inversion techniques. Note that, in practical applications, empirical or statistical correlations between different physical properties may exist, but their specific form may be unknown. In this situation, one can use a method of joint inversion, which does not require a priori knowledge about specific empirical or statistical relationships between the different model parameters and/or their attributes. This approach to the joint inversion of multimodal geophysical data uses Gramian spaces of model parameters and Gramian constraints, computed as determinants of the corresponding Gram matrices of the multi-modal model parameters and/or their attributes. This method, recently introduced by Zhdanov et al. (2012), has been shown to be a generalized method of joint inverting any number and combination of geophysical datasets, and includes extant methods based on correlations and/or structural constraints of the multiple physical properties as special case. The method is illustrated by two case studies. We present the results of joint inversion of airborne gravity gradiometer (AGG) and magnetic data collected by Fugro Airborne Surveys in the area of McFaulds Lake located in northwestern Ontario. We also jointly invert airborne magnetic and electromagnetic data from the Lac de Gras region of the Northwest Territories of Canada. These case studies demonstrate how joint inversion using Gramian constraints may enhance subsurface imaging of the mineral targets.

As part of a CO2 reservoir exploration programme, NEOS acquired and analysed a variety of geophysical data within a ~2900 km2 region of the northern Raton Basin of southern Colorado (Figure 1a). The purpose of the survey was to better define the basin architecture and identify structures that may serve as traps for the CO2 gas. In addition to the Z-axis Tipper Electromagnetic (ZTEM) and magnetotelluric (MT) data discussed here, magnetic data were collected and publicly available ground gravity and satellite remote sensing data were analysed. We also had limited access to proprietary two-dimensional (2D) seismic and well data. Note that the acquisition area contains significant topography with elevations ranging from 1500 m above sea level in the basin to mountain peaks with elevations exceeding 4000 m.

To realize the full potential of a geophysical data set, and resolve interpretation ambiguities, the data must be integrated with other geological and complementary geophysical data. Seismic velocity and electrical resistivity are rock properties that both depend on lithology and fluid content: we show that valuable insight can be gained by integrated interpretation of high-resolution velocity and resistivity models produced by inversion and imaging of broadband dual-sensor (GeoStreamer) and Towed Streamer EM data respectively. Broadband dual-sensor seismic data enables high-resolution velocity model building and depth imaging using reflections, refractions and sea-surface multiples. As a result of advances in algorithms, workflows and high performance computing it is fast becoming routine to produce high resolution and accurate velocity models for large-scale 3D broadband dual-sensor data-sets as part of the depth imaging workflow. Thus seismic velocity is a rock property that can now be determined with sufficient resolution and precision to be of use to an interpreter in an exploration setting, before more detailed quantitative interpretation studies are undertaken. Marine Controlled Source EM (CSEM) data has been used extensively to improve the chance of success in the search for hydrocarbons given that accumulations of oil and gas can be characterized by increased resistivity. CSEM data have been used mostly to de-risk prospects. By using a Towed Streamer EM system it is possible to acquire CSEM data efficiently to determine the sub-surface resistivity reliably at both regional and prospect scales.

The E&P business is most often rife with risks and uncertainties. The capability of recognizing and quantifying these uncertainties and associated risks are the key to success. For consistent exploration project risk analysis, a standard discounted cash-flow approach, combining the geological risk, Minimum Economic Field Size (MEFS), resource size distribution, development cost, rate streams, commodity price, discount rate and cash flow estimation, has been used. This task requires highly skilled geoscientists, reservoir, facility, drilling engineers and economists to estimate field development costs, generate the economic indicator to rank the exploratory prospects’ potential success and to support the informed business investment decisions. The ‘Exploration Success’ contains two main variables: (1) Probability of geologic success (Pg), and (2) Probability of economic success (Pe). To remove sub-economic volumes from the volumetric distribution, the industry uses the estimation of minimum required (break-even) resources for the full project life-cycle, considering the most likely development scenario in exploration projects. For appraisal and pre-development projects, the minimum required resources are used to benchmark the confidence level of already discovered resources with their chance of success to be economically viable. Despite several contributions made in the past and available in the literature, to the best of the author’s knowledge, most often a deterministic MEFS value is being used for the exploration project risk assessment. This single MEFS value does not allow for the capturing of the risks associated with the different input parameters uncertainties, which are used for the MEFS estimation. Therefore, in this paper, we are reviewing and systematically describing the appropriate MEFS estimation methodology. The influence of key parameters has been analysed through an example, using different MEFS methods, to demonstrate their importance for MEFS estimation. The comparison of results allows clear judgmental insights into the problem and its related uncertainties, understanding and quantifying the exploration risks and hence the chance of overall economic success.