Strength evaluation of granite block samples with different predictive models

Over the last decade, application of soft computing techniques has rapidly grown up in different scientific fields, especially in rock mechanics. One of these cases relates to indirect assessment of uniaxial compressive strength (UCS) of rock samples with different artificial intelligent-based methods. In fact, the main advantage of such systems is to readily remove some difficulties arising in direct assessment of UCS, such as time-consuming and costly UCS test procedure. This study puts an effort to propose four accurate and practical predictive models of UCS using artificial neural network (ANN), hybrid ANN with imperialism competitive algorithm (ICA–ANN), hybrid ANN with artificial bee colony (ABC–ANN) and genetic programming (GP) approaches. To reach the aim of the current study, an experimental database containing a total of 71 data sets was set up by performing a number of laboratory tests on the rock samples collected from a tunnel site in Malaysia. To construct the desired predictive models of UCS based on training and test patterns, a combination of several rock characteristics with the most influence on UCS has been used as input parameters, i.e. porosity (n), Schmidt hammer rebound number (R), p-wave velocity (V_p) and point load strength index (Is(50)). To evaluate and compare the prediction precision of the developed models, a series of statistical indices, such as root mean squared error (RMSE), determination coefficient (R²) and variance account for (VAF) are utilized. Based on the simulation results and the measured indices, it was observed that the proposed GP model with the training and test RMSE values 0.0726 and 0.0691, respectively, gives better performance as compared to the other proposed models with values of (0.0740 and 0.0885), (0.0785 and 0.0742), and (0.0746 and 0.0771) for ANN, ICA–ANN and ABC–ANN, respectively. Moreover, a parametric analysis is accomplished on the proposed GP model to further verify its generalization capability. Hence, this GP-based model can be considered as a new applicable equation to accurately estimate the uniaxial compressive strength of granite block samples.

     

HYDROCARBON MIGRATION AND CHARGE HISTORY IN THE KARANJ,PARANJ AND PARSI OILFIELDS,SOUTHERN DEZFUL EMBAYMENT,ZAGROS FOLD-AND-THRUST BELT,SW IRAN

This study investigates the charge history of the Oligocene – Lower Miocene Asmari Formation reservoir at three oilfields (Karanj, Paranj and Parsi) in the southern Dezful Embayment, SW Iran, from microthermometric analyses of hydrocarbon-bearing fluid inclusions. The Asmari Formation reservoir was sampled in seven wells at depths of between 1671.5 and 3248.5 m; samples from three of the wells were found to be suitable for fluid inclusion analyses. The samples were analyzed using an integrated workflow including petrography, fluorescence spectroscopy, Raman microspectroscopy and microthermometry. Abundant oil inclusions with a range of fluorescence colours from near-yellow to near-blue were observed. Based on the fluid inclusion petrography, fluorescence and microthermometry data, two episodes of oil charging into the reservoir were identified: 7 to 3.5 Ma, and 3.5 to 2 Ma, respectively. Fluid inclusions in general homogenized at temperatures between 112 and 398°C and with salinities of 14 to 23 wt.% NaCl equivalent. Based on the burial history, the Albian Kazhdumi and Paleogene Pabdeh Formation source rocks in the study area have not reached the gas generation window. The abundant fluid inclusions containing gas-liquid phase observed in the Asmari samples studied are therefore inferred to have been derived from secondary oil-to-gas cracking which resulted from Late Pliocene uplift.     

PREDICTION OF OPEN FRACTURES IN THE ASMARI FORMATION USING GEOMETRICAL ANALYSIS: AGHAJARI ANTICLINE,DEZFUL EMBAYMENT,SW IRAN

Reservoir quality in the carbonates of the late Oligocene – early Miocene Asmari Formation at oilfields in SW Iran is enhanced by the presence of a well‐developed fracture network. In anticlinal structures, fracture density is partly controlled by geometrical parameters such as the fold curvature. In this study, a geometrical analysis of the Asmari Formation at the NW‐SE oriented Aghajari Anticline in the Dezful Embayment is presented, and is based on inscribed circle and curvature analyses of the fold. Iso‐curvature and fracture potential maps of the Asmari Formation based on the geometrical analysis are compared to the results of fracture density logs determined from image logs at four widely‐spaced wells, and to dynamic mud loss data. The geometrical analysis demonstrates that in areas of high curvature (such as the SE and NW parts of the SW limb of the Aghajari Anticline and the central part of the NE limb), the fracture density is high. Regions of high curvature (in plan or section view) have the greatest potential to develop open fractures. The predicted fracture density distribution based on the geometrical analysis of the Asmari Formation is in good agreement with actual fracture data from the four wells and with mud loss data from the Aghajari oilfield.