Investigating the effect of data quality on time domain electromagnetic discrimination
Using field data and numerical simulations we investigate the effect of data quality on time domain electromagnetic discrimination. Data quality decreases when measurements contain responses not accounted for by our mathematical modelling. This can include instrument noise, inaccurately reported position and orientation information, geologic contributions to the signal, and loss of validity of the forward modelling. Survey design is critical to data quality in order to have sufficient sampling of data anomalies, and also to ensure that each target is illuminated such that both the axial and transverse components of the polarization can be excited and measured. For dipole model based discrimination algorithms, success is contingent upon the accuracy with which the components of the polarization tensor can be estimated. Field data from different survey modes are analysed to identify noise sources and provide quantitative estimates of the noise in each survey. Inversion results show that increased noise levels lead to greater spread in recovered parameters. Monte Carlo simulations are performed in order to investigate the importance of other data quality factors. Analysis of inversion results from the simulations show that anomaly size, signal to noise ratio, positioning error, line spacing and station spacing all play a role in the spread of recovered parameters. Through the analysis of our simulation results we propose a figure of merit as a means of quantifying different data quality factors with a single number and relate this number to the accuracy with which parameters can be estimated.
Application of a library based method to time domain electromagnetic data for the identification of unexploded ordnance
A simple library based algorithm for the identification of unexploded ordnance is tested on time domain electromagnetic data. A library of polarization tensors is generated from data acquired on a test stand over a collection of different UXO. The objective of the algorithm is to determine which target of the library is most likely to have produced an observed data anomaly. For each target in our library a non-linear inverse problem is solved for the position and orientation that minimizes the least-squares difference between the observed data anomaly and the data predicted from each target. This technique avoids direct inversion for polarization tensor, making it feasible for cases where sensor data quality may not be sufficient to support confident estimation of model parameters. For cases where the background soil response is significant, we also invert for the t −1 decay characteristic of viscous remnant magnetic soil. We present results of preliminary tests of the library technique to Geonics EM63 time domain electromagnetic data collected on a test plot seeded with UXO. These tests demonstrate an excellent ability to accurately identify isolated targets. Misidentification of single targets occur when data anomalies have low signal to noise ratios or when targets within the library have similar polarization tensors. © 2006 Elsevier B.V. All rights reserved.
Predicting the gas resource potential in reservoir C-sand interval of Lower Goru Formation, Middle Indus Basin, Pakistan
Abstract The integrated study of seismic attributes and inversion analysis can provide a better understanding for predicting the hydrocarbon-bearing zones even in extreme heterogeneous reservoirs. This study aims to delineate and characterize the gas saturated zone within the reservoir (Cretaceous C-sand) interval of Sawan gas field, Middle Indus Basin, Pakistan. The hydrocarbon bearing zone is well identified through the seismic attribute analysis along a sand channel. The sparse-spike inversion analysis has efficiently captured the variations in reservoir parameter (P-impedance) for gas prospect. Inversion results indicated that the relatively lower P-impedance values are encountered along the predicted sand channel. To further characterize the reservoir, geostatistical techniques comprising multiattribute regression and probabilistic neural network (PNN) analysis are applied to predict the effective porosity of reservoir. Comparatively, the PNN analysis predicted the targeted property more efficiently and applied its estimations on entire seismic volume. Furthermore, the geostatistical estimations of PNN analysis significantly predicted the gas-bearing zones and confirmed the sand channel as a major contributor of gas accumulation in the area. These estimates are in appropriate agreement with each other, and the workflow adopted here can be applied to various South Asian regions and in other parts of the world for improved characterization of gas reservoirs.