Abstract

The evaluation of carbonate rocks with fractures, caves, and pores is of great significance in the search for reservoir sweet spots and the prediction of reservoir productivity. With the advancement of exploration and development technology, the targets of oil and gas exploration move to deep high temperature, high pressure (HPHT) formations drilled with oil-based mud systems. The existing fracture evaluation methods often rely on dipole acoustic logging, electrical or acoustic formation micro-imaging, which utilize the difference of rock and pore fluid petrophysical properties for fracture detection, but the adverse HPHT conditions are a huge challenge to evaluate reservoir structure by such means.

The tracer imaging technology (TIT) which utilizes pulsed neutron technology and tagged proppant containing high absorption cross-section element has been proposed for crack evaluation after hydraulic fracturing, but a quantitative evaluation of crack parameters, due to their low sensitivity caused by neutron self-shielding, has not been feasible. In this paper, the combination of the new pulsed neutron tool with multi-detector array design and oil-based mud with high absorption cross-section element is used to achieve the crack parameter evaluation in carbonate reservoirs under oil-based mud invasion condition via tracer element imaging. The special oil-based mud is injected into the carbonate formation through the borehole to enhance the difference of the nuclear properties between crack and rock. A multi-detector array tool that contains four gamma detectors arranged in a ring with 90 degrees between detectors is adopted to acquire capture the gamma spectrum in different orientations. Here, a new crack inversion method adopting a joint of the multi-element characteristic peak is used to eliminate the influence of neutron self-shielding to improve the response sensitivity of crack and calculation accuracy. The new method is suitable for all pore fluid types. Meanwhile, the effect of formation backgrounds which consist of formation matrix, pore fluid, and borehole fluid on the quantitative evaluation is analyzed and discussed for limitations of this method. To improve the recognition accuracy of the parameters in the image, the digital imaging recognition method based on artificial intelligence is applied in crack imaging for the information extraction of crack orientation. The effect of formation background on the quantitative evaluation of crack parameters is analyzed and discussed.

Quantitative evaluation of carbonate with fractures, caves, and cavities can be realized with the new tracer imaging technology, which eliminates the saturation effect caused by neutron self-shielding to improve the calculation precision of fracture width. Finally, an example of carbonate formation with multiple cracks and formation background is simulated utilizing a Monte Carlo N-Particle transport model (MCNP). The calculation results of the crack density and crack width are presented and the crack orientation is determined from crack imaging, which is consistent with the model set. The result verifies the feasibility of the method.

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