The geomechanical characteristics of a drill formation are uncontrollable factors that are crucial to determining the optimal controllable parameters for a drilling operation. In the present study, data collected in wells drilled in the Marun oilfield of southwestern Iran were used to develop adaptive network-based fuzzy inference system (ANFIS) models of geomechanical parameters. The drilling specific energy (DSE) of the formation was calculated using drilling parameters such as weight-on-bit (WOB), rate of penetration (ROP), rotational speed of drilling string (RPM), torque, bit section area, bit hydraulic factor, and bit hydraulic power. A stationary wavelet transform was subsequently used to decompose the DSE signal to the fourth level. The approximation values and details of each level served as inputs for ANFIS models using particle swarm optimization (PSO) algorithm and genetic algorithm (GA). As model outputs, the Young’s Modulus, uniaxial compressive strength (UCS), cohesion coefficient, Poisson’s ratio, and internal friction angle were compared to the geomechanical parameters obtained from petrophysical logs using laboratory-developed empirical relationships. Both models predicted the Young’s modulus, UCS, and cohesion coefficient with high accuracy, but lacked accuracy in predicting the internal friction angle and Poisson’s ratio. The root mean square error (RMSE) and determination coefficient (R²) were lower for the ANFIS-PSO model than for the ANFIS-GA model, indicating that the ANFIS-PSO model presents higher accuracy and better generalization capability than the ANFIS-GA model. As drilling parameters are readily available, the proposed method can provide valuable information for strategizing a drilling operation in the absence of petrophysical logs.

The first step in mining activities is rock excavation in both mine development and production. Constant pressure for cost reduction and creating an improved/safe work environment for personnel has naturally resulted in increased use of mechanical excavation systems in many mining operations. Also, mechanical excavation and mining is more compatible with automation, meaning possibility of reduction in number of people in the active underground mines. This factor plays a major role in selection of mining systems especially considering the dire shortage of skilled labour in the industry. While these systems are an integral part of mining activities in underground soft rock mining (coal, salt, potash, trona etc.), there is a need for developing new approaches and machinery for use in the underground hard rock mining. This paper will offer a review of current and emerging technologies for mechanical hard rock excavation, including disc cutting technology, drag picks, mini-disc, and activated/oscillating disc cutter. A review of general guidelines for assessment of the potentials of new research and development on this topic and evaluation of emerging technologies for a specific mining application will also be offered.

The first phase of any hydraulic fracturing (HF) operation is to select carefully the best layers and wells based on a specific set of criteria. Selecting HF candidates plays a pivotal role in achieving the desired objective such that any attempt to execute this treatment fails without performing comprehensive studies in this regard. In this paper, data from 3 wells in a fractured carbonate reservoir in one of the biggest oilfields of Iran are studied for selecting the best HF candidate. Of these wells, which are comprised of 45 evaluated layers, the best candidate wells/layers are selected from a geomechanical point of view. Initially, a new fracability index appropriate for carbonate reservoirs, namely total fracability index (TFI), is defined. This index integrates mechanical properties and horizontal stresses into an equation. In the next step, four main criteria, namely layer thickness, TFI, natural fractures state (including orientation and density), and fracture confinement, are employed to choose the candidate layers in the wells. Lastly, having used these criteria and analytic hierarchy process, candidate intervals are selected and a hydraulic fracturing simulation model (P3D) is used for evaluation.