No abstract

Evaluation of hydrocarbon reservoir requires classification of petrophysical properties from available dataset. However, characterization of reservoir attributes is difficult due to the nonlinear and heterogeneous nature of the subsurface physical properties. In this context, present study proposes a generalized one class classification framework based on Support Vector Data Description (SVDD) to classify a reservoir characteristic-water saturation into two classes (Class high and Class low) from four logs namely gamma ray, neutron porosity, bulk density, and P-sonic using an imbalanced dataset. A comparison is carried out among proposed framework and different supervised classification algorithms in terms of g-metric means and execution time. Experimental results show that proposed framework has outperformed other classifiers in terms of these performance evaluators. It is envisaged that the classification analysis performed in this study will be useful in further reservoir modeling.

A spin-1 Heisenberg model on trimerized Kagomé lattice is studied by doing a low-energy bosonic theory in terms of plaquette-triplons defined on its triangular unit-cells. The model considered has an intra-triangle antiferromagnetic exchange interaction, J (set to 1), and two inter-triangle couplings, J > 0 (nearest-neighbor) and J (next-nearest-neighbor; of both signs). The triplon analysis performed on this model investigates the stability of the trimerized singlet ground state (which is exact in the absence of inter-triangle couplings) in the J -J plane. It gives a quantum phase diagram that has two gapless antiferromagnetically ordered phases separated by the spin-gapped trimerized singlet phase. The trimerized singlet ground state is found to be stable on J = 0 line (the nearest-neighbor case), and on both sides of it for J = 0, in an extended region bounded by the critical lines of transition to the gapless antiferromagnetic phases. The gapless phase in the negative J region has a coplanar 120• -antiferromagnetic order with √ 3 × √ 3 structure. In this phase, all the magnetic moments are of equal length, and the angle between any two of them on a triangle is exactly 120• . The magnetic lattice in this case has a unit-cell consisting of three triangles. The other gapless phase, in the positive J region, is found to exhibit a different coplanar antiferromagnetic order with ordering wavevector q = (0, 0). Here, two magnetic moments in a triangle are of same magnitude, but shorter than the third. While the angle between two short moments is 120• − 2δ, it is 120• + δ between a short and the long one. Only when J = J , their magnitudes become equal and the relative-angles 120• . The magnetic lattice in this q = (0, 0) phase has the translational symmetry of the Kagomé lattice with triangular unit-cells of reduced (isosceles) symmetry. This reduction in the point-group symmetry is found to show up as a difference in the intensities of certain Bragg peaks, whose ratio, I (1,0) /I (0,1) = 4 sin 2 ( π 6 + δ), presents an experimental measure of the deviation, δ, from the 120• order.

This paper proposes a complete framework consisting pre-processing, modeling, and postprocessing stages to carry out well tops guided prediction of a reservoir property (sand fraction) from three seismic attributes (seismic impedance, instantaneous amplitude, and instantaneous frequency) using the concept of modular artificial neural network (MANN). The dataset used in this study comprising three seismic attributes and well log data from eight wells, is acquired from a western onshore hydrocarbon field of India. Firstly, the acquired dataset is integrated and normalized. Then, well log analysis and segmentation of the total depth range into three different units (zones) separated by well tops are carried out. Secondly, three different networks are trained corresponding to three different zones using combined dataset of seven wells and then trained networks are validated using the remaining test well. The target property of the test well is predicted using three different tuned networks corresponding to three zones; and then the estimated values obtained from three different networks are concatenated to represent the 1 predicted log along the complete depth range of the testing well. The application of multiple simpler networks instead of a single one improves the prediction accuracy in terms of performance evaluators-correlation coefficient, root mean square error, absolute error mean and program execution time. Then, volumetric prediction of reservoir properties is carried out using calibrated network parameters. This stage is followed by post-processing to improve visualization. Thus, a complete framework, which includes pre-processing, model building and validation, volumetric prediction, and post-processing, is designed for successful mapping between seismic attributes and a reservoir characteristic. The proposed framework outperformed a single artificial neural network in terms of reduced prediction error, program execution time and improved correlation coefficient as a result of application of the MANN concept.

The aim of the present study is to analyse the occurrences of future earthquakes using forecasting techniques from past seismicity in northeast India (latitude 20 N-31 N and longitude 87 E-97 E). The present study applies two types of retrospective binary forecasting. The first one is the pattern informatics (PI) method and the other is the Relative Intensity (RI) method. These techniques quantify the spatio-temporal seismicity rate changes in the historic seismicity of the study region. For this purpose, a uniform and complete earthquake catalogue in moment magnitude (M w > 3) is prepared. The resulting binary forecasts are evaluated with the relative operating characteristics (ROC) diagram. The ROC diagram quantifies the results in terms of a hit rate (fraction of events that are successfully forecasted) versus a false alarm rate (no event occurs in a hotspot box). Evaluation of forecasting results using ROC diagram is more protective than maximum likelihood tests. The result gives a regional seismogenic map where earthquakes are likely to occur during a specified period in the future. The recent India-Nepal border earthquake of 18 September 2011 occurred in one of the forecasted regions. These techniques have been applied for the first time to the Indian subcontinent.