ASEG Extended Abstracts - 24th International Geophysical Conference and Exhibition – Geophysics and Geology Together for Discovery, 2015

We present a feasibility study of using passive seismic data for imaging of diffractors. Imaging and characterisation of seismic diffractors is important for many applications of seismic methods, including carbon geosequestration, since in sedimentary setting the diffractors are associated with terminations of layers at faults, as well as edges of the zones altered through the reservoir depletion or fluid (e.g. CO2) injection. One of the findings is that the diffracted waves from ambient sources can be sometimes incorrectly interpreted as active seismic sources that might lead to wrong conclusions about induced seismicity of processes generating the ambient noise, such as injection of fluids in the subsurface.

Seismic monitoring at injection sites (e.g., CO2 sequestration, hydraulic fracturing) has become an increasingly common tool amongst oil and gas producers. The information obtained from these data is often limited to seismic event properties (e.g., location, initiation time, moment tensor), the accuracy of which greatly depends on the assumed or estimated elastic velocity models. However, estimating accurate 3D velocity models from passive array data remains a challenging problem. Extended imaging conditions (eICs) for passive wave-equation imaging algorithms represent a key step towards generating - and verifying - elastic velocity models. By extending imaging conditions away from zero-lag in time and space we can better evaluate the focusing of a given event based on the principle that waves focus at zero lag only when the velocity models are “correct”. We demonstrate that given an elastic medium and multi-component recordings, we can propagate and correlate microseismic P- and S-wavefield modes to compute eICs for P- and S- velocity perturbations. We observe that the maximum correlation deviates from the zero-lag in time and space for a P/S cross-correlation imaging condition when using an incorrect P- and/or S-wave velocity, and thus there is sensitivity to velocity error not observable when using individual wavefield components.

In this paper we develop a new method on the basis of the two exist two methods, using both P and S waves recorded with surface array and borehole data. The new microseismic location method take the full advantage of Generalized Pattern Search Method and Simulated Annealing Method, improving the location accuracy. We use this method to do the cast study. Consequently, using both P and S waves information in the location technique reduces the position uncertainty as compared to single P or S wave relative location. And it will be more applicable to low signal-to-noise ratios data.

An improved method is proposed in this paper for microseismic source location. The primary goal of this method is to improve the location accuracy for microseismic events with low signal-to-noise ratios (SNR). In contrast to the prevalent location approach, two innovations are implemented in the proposed method. First, instead of using the hodogram, the source azimuth is estimated from a probability distribution function, and second, a new objective function is employed in grid search algorithm to find the source position. The proposed method has been tested using synthetic data examples. The results show that, for these cases, the absolute errors of the estimated source azimuth and position are less than 1° and 3m, respectively, which proves that an improvement in location accuracy can readily be achieved using the proposed method.

This paper describes the factors that control elastic properties of organic shale, which is crucial for exploration and successful gas production from unconventional reservoirs. Mechanical and dynamic elastic properties are main shale characteristics that are not yet well understood as there have been a limited number of investigations involving organic rich shale samples. Synthetic shale core samples whose clay mineralogy, non-clay mineral content and Total Organic Carbon (TOC) content are known can be used to study variations of elastic parameters in a controlled experimental environment including in-situ stress conditions.

More than 20 synthetic shale samples were created for our investigations under reservoir stress conditions with different mineral composition and TOC percentage. Ultrasonic transducers were used to measure body wave velocities, which were then used to calculate the elastic properties of different shale samples. The results demonstrate that P- and S-wave velocities vary with changing TOC under isotropic stress conditions. It is shown that the velocities of P-and S-waves are inversely proportional to TOC content. In addition, the increase in the TOC produced a decrease in density from approximately 2.4 g/cc to 2.15 g/cc and increase in porosity from approximately 16% to 20%.

In the process of formation evaluation for tight reservo irs, extracting quantitative information of kerogen is a potentially important factor. Moreover, Total Organic Carbon (TOC) is not strongly correlated with geophysical well logging data. In this paper, a combinatory algorithm for nonlinear regression based on Empirical Mode Decomposition (EMD) and Support Vector Regression (SVR) is proposed. On the basis of depth matching, sensitive well logging parameters are preferred by core calibration. That, which means should be used to denoise, is a key issue for acquiring precise and high quality data. Then, intrinsic mode functions (IMF) decomposed by EMD algorithm is established and applied for denosing. Further, denoised data is classified into two categories, one for training and the other for validating. Aiming for TOC predicting model, SVR is implemented both for training and predicting, and simultaneously some conventional methods such as AlogR, back propagation artificial neural networks(BP-ANN), and multiple linear regressions are also exerted for comparisons. The result shows that EMD-SVR is the best solution for TOC predicting, with the highest correlation coefficient and the smallest mean squared errors. Likewise, this algorithm is applicable for other reservoirs like shale gas.

The SPM effect is not traditionally associated with glacial tills. However, effects of viscous magnetization, i.e. superparamagnetism, are observed in many places in Northern Finland in high sensitivity and low frequency time domain ground EM surveys. These effects are typically observed on late time channels and they have a very good correspondence with magnetic anomalies. Usually there is also a clear reverse frequency domain RE component anomaly observed simultaneously with the SPM effect. In Ni ore prospecting it is essential to be able to recognize SPM effect because it has a response similar to a deep seated massive nickel ore body i.e. a deep seated very high conductivity conductor. Characteristic feature for SPM effect in time domain dB/dt data is its 1/t decay which can be used as a means to recognize this phenomenon. We also discuss alternative methods to recognize SPM effect.

The moisture content of iron ore is a critical factor in determining its subsequent processing, transportation, quality and general handling. In particular, high moisture content can lead to extremely dangerous liquefaction of the ore during sea-transportation, with major financial and safety implications. NMR has long been used to measure moisture content in rocks but the presence of magnetic materials such as iron affects the NMR signal leading to data that cannot be interpreted by known methods.

There has recently been interest in determining how the properties of the rock affect the NMR signal, particularly where there are different concentrations of magnetic materials or rocks with different mineralogical properties. There has, however, been less work in determining how the measurement techniques should be changed to accommodate these systems.

In this work we measure iron rich rock cores and show the importance of selecting an appropriate echo time in the CPMG sequence. We show that longer echo times can lead to estimations of a T2 distribution that is not representative of the system. This in turn would lead to inaccurate measurements of the water content. We demonstrate the importance of short echo times and show that for the systems studied in this work, an upper limit of 0.4 ms should be imposed.

Localised Smart interpretation (LSI) is a method that infers a statistical model, which describes a relation between the knowledge of a geologist (as quantified by geological interpretation) and the available information (such as geophysical data, well log data, etc.) that a geologist uses when he/she interprets. This model is then used to perform semi-automatic geological interpretation wherever the same kinds of attributes, as used for the initial interpretation, are available. The statistical model is inferred using a combination of a regularized least squares method and cross validation. In this study, we demonstrate the applicability of the method to predict the depth to a low resistivity subsurface layer, based on interpretations from a geological expert, using a 19-layered resistivity model obtained from inversion of airborne electromagnetic (SkyTEM) data. This study shows that LSI is capable of making prediction with great accuracy. The method is fast and is able to handle large amounts of data of different origin, which suggest that the method may become a very useful approach to assist in geological modelling, based on increasingly large amounts of data of different nature.

Directly interpreting total-field magnetic anomaly data in the South China Sea (SCS) can be difficult because of the complex patterns associated with low-latitude anomaly projection and the presence of remanent magnetization. Additional difficulty arises from the fact that the ambient field direction, thus, the total-field anomaly projection direction, varies over a wide range in the area. To alleviate these difficulties, we present a strategy by using magnetic amplitude data analyses and inversion. Equivalent source processing is used to calculate the amplitude data in the space domain since the wavenumber-domain method is no longer applicable due to low and highly variable inclination. The amplitude data serve the role of reduction-to-pole (RTP) transformation for structural interpretation. We then carry out the amplitude inversion to generate a 3D subsurface distribution of effective susceptibility. The inversion results show that this approach is feasible and effective in SCS.

An exploratory 3-D model of the electrical conductivity structure of the Australian continent is presented. The model is derived from the inversion of vertical magnetic-field transfer functions from the Australia-wide Array of Geomagnetic Stations.

The model reveals conductivity differences beneath Archaean cratons in Western Australia, enhanced-conductivity anomalies between Archaean cratonic regions and beneath Phanerozoic terranes in eastern Australia.

Breiner (1973) described a method to separate induced magnetisation from remanent magnetisation of a drill core or hand sample using a total field magnetometer, thereby allowing the Konigsberger ratio (Q) to be calculated. However, the method does not seem to be in general use, and nor has there been any improvement to the method despite recent developments that greatly facilitate data acquisition using handheld devices or notebook computers. Here a new fluxgate based pendulum instrument is described that allows a more controlled implementation of Breiner (1973)’s method.

The instrument accommodates samples of varying sizes although best results are yielded using regular, cubic or cylindrical, shaped samples. The instrument is portable and powered from a USB port of a notebook or similar computer. Sensitivity is high enough to yield accurate results for rocks that cause significant magnetic anomalies (~Am⁻¹). However, for high Q rocks susceptibility is lost in the noise, and likewise for low Q rocks, remanence is lost in the noise.

The instrument is designed to quickly screen core samples at the drill site, or in a core shed, to alert the exploration team if significant remanence is present in which case the magnetic model of the target may require reconsidering. The instrument is not a replacement for laboratory measurements but potentially should save exploration costs by indicating when laboratory measurements should be undertaken.

We present a cooperative inversion approach for acoustic impedance using seismic and magnetotelluric data. In this approach, the magnetotelluric data, sensitive to the resistivity of rocks are used to get the large scale background spatial trends of the acoustic impedance model, while the seismic data are used to get the small-scale features. The connections between resistivity and elastic properties of rocks are obtained from petrophysical relationships derived from borehole data. Structural constraints derived from seismic are used to improve the magnetotelluric inversion. We present an application of this technique to synthetic data derived from previous interpretation of seismic and magnetotelluric models in a mineral province. The synthetic example shows how an improved result is obtained using our cooperative inversion workflow.

Hydrothermal Au - Cu mineralisation at Majors Creek, NSW has led to the formation of disseminated sulphides throughout the host granodiorite body. Mineralisation in overburden and shallow bedrock occurs in sparse concentration settings such as quartz veins and potassic alteration. Distinguishing between alterations zones, mineralis ing features and the fresh-weathered rock boundary is paramount to explorers.

A combination of DC electrical resistivity and CT scanning was employed to delineate the weathered/fresh rock boundary, potential mineralising features and areas of differing alterations. A 500 metre survey line was constructed over a known area of mineralisation and passed directly over a drill core sample. CT scanning data will define pore space characteristics of alteration and weathering states of the host granodiorite.

This study has the potential to spark future researching into shallow surface exploration throughout the Major’s Creek area, building on a potential relationship between, pore space, apparent resistivity and overburden-bedrock characteristics.

We present a large-scale study of the relationship between dense airborne SkyTEM resistivity data and sparse lithological borehole data.

Airborne electromagnetic (AEM) data contains information about subsurface geology and hydrologic properties; however extracting this information is not trivial. Today, geophysical data is used in combination with borehole data to create detailed geological models of the subsurface. The overall statistical relationship is, however, not widely known. The objective of this study is to develop a method for understanding the relationship between petrophysical properties and lithology, and apply this to get a better understanding of large-scale petrophysical structures of the subsurface.

The data sampling is carried out in a scheme where data is interpolated onto the position of the boreholes. This allows for a lithological categorization of the interpolated resistivity values, revealing different distribution functions for lithological categories.

A very large and extensive dataset is available in Denmark through the national geophysical and borehole databases. These databases contain all geophysical and borehole data in Denmark and covers a large part of its surface. By applying the proposed algorithm to all available airborne electromagnetic data, detailed maps of the large-scale resistivity-lihology structures on a National scale in Denmark are constructed.

We discuss the theoretical development of the measurement of the T2* component from a surface nuclear magnetic resonance (SNMR) experiment using superconducting quantum interference devices (SQUIDS) as a point B-field receiver.

We discuss the differences between point receivers compared to traditional coincident-loop receivers, and demonstrate the first measurements of T2* with a SQUID sensor at the hydrogeophysical test site in Schillerslage, Germany.

The Anangu Pitjantjatjara Yankunytjatjara (APY) Lands of South Australia is an arid environment and the population relies largely on groundwater resources for potable water and agricultural needs. Historically, locating productive wells in the region has been hit-and-miss and even if a water source was found, the quality may be unreliable. In this project, we seek to improve the water security in the APY lands by demonstrating that surface Nuclear Magnetic Resonance (NMR) and Time-Domain Electromagnetic (TEM) geophysical measurements are able to map local aquifers and quantify ground water resources, thereby optimizing site selection for potential future wells. Surface NMR is directly sensitive to water and TEM measurements detecting the electrical conductivity structure and able to image the subsurface over large areas - all entirely non-invasively and with minimal risk of disturbing sites of importance to the local Aboriginals.

The applicability of magnetic resonance sounding in mapping the water content and the hydrological properties of the sub-surface in industrialized areas is severely limited by electromagnetic noise. Efficient ways of mitigating the noise must be developed before the technique can become a ubiquitous tool. In this paper we demonstrate an instrument developed for efficient mapping of noise at a given site prior to a magnetic resonance sounding. The instrument consists of two small induction coils connected to a digital oscilloscope controlled by a PC. Using the instrument, measurements of the electromagnetic noise are easily performed at several places within the site. Signal processing of the measurements provide a quantified understanding of the contributions from different noise sources, primarily powerline harmonics and impulsive noise. Further the spatial distributions of the noise components are also obtained. Based on this knowledge the optimum spot for a magnetic resonance sounding with the least distortion by noise can be identified. The instrument is now in routine use at the Hydrogeophysics group at Aarhus University.

Surface Nuclear Magnetic Resonance (NMR) is a noninvasive geophysical technique providing the ability to image and investigate aquifer properties. In order to produce reliable images and interpretations of subsurface properties accurate modelling of the underlying physics is required. In magnetic environments, where the background magnetic field varies spatially, challenges can arise that lead to difficulty accurately modelling the excitation process and interpreting the signal’s time dependence. We demonstrate using field data collected in the Anangu Pitjantjatjara Yankunytjatjara (APY) Lands of South Australia that neglecting the influence of the magnetic environment can significantly alter the final images and interpretation of the subsurface structure and properties.