Assessment of drought hazard impact on wheat cultivation using standardized precipitation index in Iran

The assessment of drought hazard impacts on wheat cultivation as a strategic crop in Iran is essential for making mitigation plans to reduce the impact of drought. Standardized precipitation index has gained importance in recent years as a potential drought indicator and is being used more frequently for assessment of drought hazard in many countries. In the present study, the calculated standardized precipitation index for 48 stations dataset in the 30-year time scale fulfilled 30 statistical matrices. The drought hazard index map was produced by sum overlaying the spatial representations of 30 statistical matrices and categorized into four levels of low, moderate, high, and very high, which demonstrated probability of drought occurrences of 10–20 %, 20–30 %, 30–40 %, and 40–50 %, respectively. Finally, after the general division of zonal statistics in drought hazard index map of Iran, major drought hazard zones were geographically classified into five zones. The statistical analysis showed a significant correlation (R ²?=?0.701 to 0.648) between drought occurrences and wheat cultivation including surface area and total production for these drought hazard zones.     

Investigation of rock quality of Shirinrud dam site by engineering seismology

At this paper, we studied about the rock quality of Shirinrud dam site by engineering seismology. Shirinrud dam site is located 80 km far from Kerman and 18 km far from Hojadk village. The dam and its constructions are established in the Bidu Formation which consists of seven rock units, and the refraction profiles were surveyed on Jb_3/2, Jb₄, and Jb₅ rock units. To evaluate the rock mass quality and basement topography at this site, nine refraction seismic profiles by primary waves and two refraction seismic profiles by secondary waves were surveyed. We used some methods such as Palmer method, the reciprocal method, plus–minus method, etc. to process and interpret data. Based on investigations, primary wave velocity in unit Jb_3/2 varies between 2,100 and 2,200 m/s, in unit Jb₄ is between 2,100 and 4,200 m/s, and in unit Jb₅ is between 2,500 and 3,000 m/s. The Q values on these three units are 0.05, 1.2, and 1.9, and the rock mass rating (RMR) values are 27.1, 40.5, and 33.5, respectively. With respect to wave velocity, Q, and RMR values, the units Jb_3/2, Jb₄, and Jb₅ are evaluated as very weak, intermediate, and weak, respectively.     

Using fuzzy TOPSIS method for mineral processing plant site selection

Site selection for the mineral processing is the most important decision made by owner that has a significant impact on the efficiency of the whole process. This is a critical decision which involves considering a number of criteria and finding the best location among feasible alternatives. Therefore, a multicriteria decision-making method is necessary to apply site-selection process to find the best location that meet desired conditions set by the selection criteria. This paper presents an application of TOPSIS method based on fuzzy sets (fuzzy TOPSIS), which is one of the broader multicriteria decision making means, used to select an appropriate site for mineral processing plant for Sangan iron ore mine (phase 2). For this purpose, at first considering the geological, technical, economical, and environmental factors, three feasible alternatives were selected for the processing plant using Geographical Information System. Then, based on the technical and experimental experiences and through judgment of the decision makers and experts, 14 criteria were established and these alternatives were evaluated. Finally, the alternatives were ranked and the best location was recommended.     

Acceleration of thermodynamic computations in fluid flow applications

Reservoir simulators model the highly nonlinear partial differential equations that represent flows in heterogeneous porous media. The system is made up of conservation equations for each thermodynamic species, flash equilibrium equations and some constraints. With advances in Field Development Planning (FDP) strategies, clients need to model highly complex Improved Oil Recovery processes such as gas re-injection and CO2 injection, which requires multi-component simulation models. The operating range of these simulation models is usually around the mixture critical point and this can be very difficult to simulate due to phase mislabeling and poor nonlinear convergence. We present a Machine Learning (ML) based approach that significantly accelerates such simulation models. One of the most important physical parameters required in order to simulate complex fluids in the subsurface is the critical temperature (T_crit). There are advanced iterative methods to compute the critical point such as the algorithm proposed by Heidemann and Khalil (AIChE J 26,769–799, 1980) but, because these methods are too expensive, they are usually replaced by cheaper and less accurate methods such as the Li-correlation (Reid and Sherwood 1966). In this work we use a ML workflow that is based on two interacting fully connected neural networks, one a classifier and the other a regressor, that are used to replace physical algorithms for single phase labelling and improve the convergence of the simulator. We generate real time compositional training data using a linear mixing rule between the injected and the in-situ fluid compositions that can exhibit temporal evolution. In many complicated scenarios, a physical critical temperature does not exist and the iterative sequence fails to converge. We train the classifier to identify, a-priori, if a sequence of iterations will diverge. The regressor is then trained to predict an accurate value of T_crit. A framework is developed inside the simulator based on TensorFlow that aids real time machine learning applications. The training data is generated within the simulator at the beginning of the simulation run and the ML models are trained on this data while the simulator is running. All the run-times presented in this paper include the time taken to generate the training data and train the models. Applying this ML workflow to real field gas re-injection cases suffering from severe convergence issues has resulted in a 10-fold reduction of the nonlinear iterations in the examples shown in this paper, with the overall run time reduced 2- to 10-fold, thus making complex FDP workflows several times faster. Such models are usually run many times in history matching and optimization workflows, which results in compounded computational savings. The workflow also results in more accurate prediction of the oil in place due to better single phase labelling.