Preparation of frost atlas using different interpolation methods in a semiarid region of south of Iran

In this research, suitability of different kriging and inverse distance weighted (IDW) methods in estimating occurrence date of frost was evaluated. Data included minimum daily air temperature values from 27 meteorological stations of Fars province in southern Iran from 18 to 45 years. Data ranges of 0 to ?1.5, ?1.5 to ?3 and below ?3°C were considered as mild, moderate and severe frost intensities, respectively. Starting with the first day of autumn, iso-occurrence days for the frost intensities and occurrence probabilities (25%, 50%, 75% and 90%) were estimated using ordinary kriging, cokriging, residual kriging type 1 (RK1), residual kriging type 2 (RK2), universal kriging and IDW methods. In these models, the errors of estimated frost intensities at different probabilities were lowest in the RK2 model, but lack of establishment of spatial structure due to long distance between stations caused the predictions not to be acceptable in some cases. In a proposed method (modified inverse distance weighted, MIDW), the trend between the first and last days of frost occurrence with earth elevation was removed, and the reminder values were estimated by (IDW) method. Although, the errors for estimated frost dates by MIDW and RK2 methods were the same, but the MIDW method did not have the spatial establishment shortcoming. Furthermore, the simplicity and practicality of the MIDW method makes it a reasonable selection.     

Mapping groundwater recharge potential zones in arid region using GIS and Landsat approaches,southeast Tunisia

The rapid growth of population and agricultural and industrial activities has caused an increase in demand on the Jeffara aquifers of Gabes (southeast Tunisia). In fact, the over-pumping of this aquifer system has resulted in water-level declines ranging from 0.25 to 1 m/year during the past three decades. The aim of this study is to identify favourable artificial recharge sites of this aquifer system based on the combined use of remotely sensed data, a geographic information system (GIS), the Shuttle Radar Topography Mission (SRTM) product and a multiple criteria decision making (MCDM) technique. The delineation of artificial recharge zones shows high to moderate potential for groundwater recharge (40%) in the Gabes region, with high precision of good potential proposed sites. Recharge processes need to account for natural conditions and ecosystems.     

Petrography and geochemistry of Silurian Niur sandstones,Derenjal Mountains,East Central Iran: implications for tectonic setting,provenance and weathering

Petrographical and geochemical studies of Silurian Niur sandstones, Derenjal Mountains, Central Iran, were carried out to infer their provenance and tectonic setting. Modal analysis data of 37 medium sand size and well-sorted samples revealed that most quartz is composed of monocrystalline grains with straight to slightly undulos extinction and about 3 % polycrystalline quartz has inclusions, such as rutile needles. The sandstones are classified as quartzarenite, sublitharenite, and subarkose types based on framework composition and geochemistry. Petrographic studies reveal that these sandstones contain quartz, feldspars, and fragments of sedimentary rocks. The detrital modes of these sandstones indicate that they were derived from recycled orogen and stable cratonic source. Major and trace element contents of them are generally depleted (except SiO₂) relative to upper continental crust which is mainly due to the presence of quartz and absence of Al-bearing minerals. Modal composition (e.g., quartz, feldspar, and lithic fragments) and discrimination diagrams based on major elements, trace elements (Ti, La, Th, Sc, and Zr), and also such ratios as La/Sc, Th/Sc, La/Co, and Th/Co, in sandstones suggest a felsic igneous source rock and quartzose polycyclic sedimentary provenance in a passive continental margin setting. Furthermore, high Zr/Sc values in these sandstones are considered as a sign of recycling. We indicated paleo-weathering conditions by modal compositions, the CIA index and Al₂O₃?+?K₂O?+?Na₂O% vs. SiO₂% bivariate for these sandstones. Based on these results, although recycling is important to increase the maturity of the Niur sandstones, humid climate conditions in the source area have played a decisive role.     

Perogenesis of granites, Sharm El-Sheikh area, South Sinai, Egypt: petrological constrains and tectonic evolution

Precambrian granites of the Sharm El-Sheikh area in south Sinai, Egypt belong to collisional and post-collisional Magmatism (610–580 Ma). The granites are widely distributed in the northern part of the Neoproterozoic Arabian-Nubian Shield. South Sinai includes important components of successive multiple stages of upper crust granitic rocks. The earliest stages include monzogranite and syenogranites while the later stages produced alkali feldspar granites and riebeckite-bearing granites. Numerous felsic, mafic dikes and quartz veins traverse the study granites. Petrographically, the granitic rocks consist mainly of perthite, plagioclase, quartz, biotite and riebeckite. Analysis results portray monzogranites displaying calc-alkaline characteristics and emplaced in island-arc tectonic settings, whereas the syenogranites, alkali-feldspar granites and the riebeckite bearing-granites exhibit an alkaline nature and are enriched in HFSEs similar to granites within an extensional regime. Multi-element variation diagrams and geochemical characteristics reinforce a post-collision tectonic setting. REEs geochemical modeling reveals that the rocks were generated as a result of partial melting and fractionation of lower crust basaltic magma giving rise to A₁ and A₂ subtype granites. They were subsequently emplaced within an intraplate environment at the end of the Pan-African Orogeny.     

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.