Hydrodynamic behavior of Kangan gas-capped deep confined aquifer in Iran

The Kangan aquifer (KA) is located beneath the Kangan gas reservoir (KGR), 2,885 m below the ground surface. The gas reservoir formations are classified into nine non-gas reservoir units and eight gas reservoir units based on the porosity, water and gas saturation, lithology, and gas production potential using the logs of 36 production wells. The gas reservoir units are composed of limestone and dolomite, whereas the non-gas reservoir units consist of compacted limestone and dolomite, gypsum and shale. The lithology of KA is the same as KGR with a total dissolved solid of 333,000 mg/l. The source of aquifer water is evaporated seawater. The static pressure on the Gas–Water Contact (GWC) was 244 atm before gas production, but it has continuously decreased during 15 years of gas production, resulting in a 50 m uprising of the GWC and the expansion of KA water and intergranular water inside the gas reservoir. The general flow direction of the KA is toward the northern coast of the Persian Gulf due to the migration of water to the overlying formations via a trust fault. The KA is a gas-capped deep confined aquifer (GCDCA) with special characteristics differing from a shallow confined aquifer. The main characteristics of a GCDCA are unsaturated intergranular water below the confining layers, no direct contact of the water table (GWC) with the confining layers, no vertical flow via the cap rock, permanent uprising of the GWC during gas production, and permanent descend of GWC during water exploitation.     

Geochemical behavior and speciation modeling of rare earth elements in acid drainages at Sarcheshmeh porphyry copper deposit,Kerman Province,Iran

Acid mine drainage is a major source of water pollution in the Sarcheshmeh porphyry copper mine area. The concentrations of heavy metals and rare earth elements (REEs) in the host rocks, natural waters and acid mine drainage (AMD) associated with mining and tailing impoundments are determined. Contrary to the solid samples, AMDs and impacted stream waters are enriched in middle rare earth elements (MREEs) and heavy rare earth elements (HREEs) relative to light rare earth elements (LREEs). This behavior suggests that REE probably fractionate during sulfide oxidation and acid generation and subsequent transport, so that MREE and HREE are preferentially enriched. Speciation modeling predict that the dominant dissolved REE inorganic species are Ln³⁺, Ln(SO₄)₂⁻, LnSO₄⁺, LnHCO₃²⁺, Ln(CO₃)₂⁻ and LnCO₃⁺. Compared to natural waters, Sarcheshmeh AMD is enriched in REEs and SO₄²⁻. High concentrations of SO₄²⁻ lead to the formation of stable LnSO₄⁺, thereby resulting in higher concentrations of REEs in AMD samples. The model indicates that LnSO₄⁺ is the dissolved form of REE in acid waters, while carbonate and dicarbonate complexes are the most abundant dissolved REE species in alkaline waters. The speciation calculations indicate that other factors besides complexation of the REE's, such as release of MREE from dissolution and/or desorption processes in soluble salts and poorly crystalline iron oxyhydroxy sulfates as well as dissolution of host rock MREE-bearing minerals control the dissolved REE concentrations and, hence, the MREE-enriched patterns of acid mine waters.     

Drought Forecasting in a Semi-arid Watershed Using Climate Signals:a Neuro-fuzzy Modeling Approach

Large-scale annual climate indices were used to forecast annual drought conditions in the Maharlu-Bakhtegan watershed,located in Iran,using a neuro-fuzzy model.The Standardized Precipitation Index（SPI） was used as a proxy for drought conditions.Among the 45 climate indices considered,eight identified as most relevant were the Atlantic Multidecadal Oscillation（AMO）,Atlantic Meridional Mode（AMM）,the Bivariate ENSO Time series（BEST）,the East Central Tropical Pacific Surface Temperature（NINO 3.4）,the Central Tropical Pacific Surface Temperature（NINO 4）,the North Tropical Atlantic Index（NTA）,the Southern Oscillation Index（SOI）,and the Tropical Northern Atlantic Index（TNA）.These indices accounted for 81% of the variance in the Principal Components Analysis（PCA） method.The Atlantic surface temperature（SST：Atlantic） had an inverse relationship with SPI,and the AMM index had the highest correlation.Drought forecasts of neuro-fuzzy model demonstrate better prediction at a two-year lag compared to a stepwise regression model.     

On lattice reduction algorithms for solving weighted integer least squares problems: comparative study

Decorrelation or reduction theory deals with identifying appropriate lattice bases that aid in accelerating integer search to find the optimal integer solution of the weighted integer least squares problem. Orthogonality defect has been widely used to measure the degree of orthogonality of the reduced lattice bases for many years. This contribution presents an upper bound for the number of integer candidates in the integer search process. This upper bound is shown to be a product of three factors: (1) the orthogonality defect, (2) the absolute value of the determinant of the inverse of the generator matrix of the lattice, and (3) the radius of the search space raised to the power of the dimension of the integer ambiguity vector. Four well-known decorrelation algorithms, namely LLL, LAMBDA, MLAMBDA, and Seysen, are compared. Many simulated data with varying condition numbers and dimensions as well as real GPS data show that the Seysen reduction algorithm reduces the condition number much better than the other algorithms. Also, the number of integer candidates, before and after the reduction process, is counted for all algorithms. Comparing the number of integer candidates, condition numbers, and orthogonality defect reveals that reducing the condition number and the orthogonality defect may not necessarily result in decreasing the number of integer candidates in the search process. Therefore, contrary to the common belief, reducing the orthogonality defect and condition number do not always result in faster integer least squares estimation. The results indicate that LAMBDA and MLAMBDA perform much better in reducing the number of integer candidates than the other two algorithms, despite having a larger orthogonality defect and condition number in some cases. Therefore, these two algorithms can speed up the integer least squares estimation problem in general and the integer ambiguity resolution problem in particular.     

Extracting tidal frequencies using multivariate harmonic analysis of sea level height time series

This contribution is seen as a first attempt to extract the tidal frequencies using a multivariate spectral analysis method applied to multiple time series of tide-gauge records. The existing methods are either physics-based in which the ephemeris of Moon, Sun and other planets are used, or are observation-based in which univariate analysis methods—Fourier and wavelet for instance—are applied to tidal observations. The existence of many long tide-gauge records around the world allows one to use tidal observations and extract the main tidal constituents for which efficient multivariate methods are to be developed. This contribution applies the multivariate least-squares harmonic estimation (LS-HE) to the tidal time series of the UK tide-gauge stations. The first 413 harmonics of the tidal constituents and their nonlinear components are provided using the multivariate LS-HE. A few observations of the research are highlighted: (1) the multivariate analysis takes information of multiple time series into account in an optimal least- squares sense, and thus the tidal frequencies have higher detection power compared to the univariate analysis. (2) Dominant tidal frequencies range from the long-term signals to the sixth-diurnal species interval. Higher frequencies have negligible effects. (3) The most important tidal constituents (the first 50 frequencies) ordered from their amplitudes range from 212 cm (M2) to 1 cm (OQ2) for the data set considered. There are signals in this list that are not available in the 145 main tidal frequencies of the literature. (4) Tide predictions using different lists of tidal frequencies on five different data sets around the world are compared. The prediction results using the first significant 50 constituents provided promising results on these locations of the world.     

On weighted total least-squares with linear and quadratic constraints

A weighted total least-squares (WTLS) approach with linear and quadratic constraints is developed. This method is according to the traditional Lagrange approach to optimize the target function of this problem. The WTLS and constrained total least-squares (CTLS) approach had been distinctively investigated, however, these two problems have not been simultaneously considered yet; furthermore, among the contributions on the CTLS problem, only Schaffrin and Felus considered linear and quadratic constraints together; nevertheless, in many practical examples, some elements of the design/coefficient matrix are fixed and should not be modified and this approach cannot deal with these cases. The main necessity of this research appears after the desirable property of the WTLS approach in preserving the structure of the design matrix was proven by Mahboub. In other words, currently, the WTLS approach is one of the most efficient methods for solving the so-called errors-in-variables model and an attempt for equipping it with constraints seems necessary. Also it is demonstrated that the additional constraints have a ’regularization role’ for ill-conditioned problems and the unconstrained solution suffers from ill-conditioning effects which give it an added advantage over the unconstrained WTLS algorithm. Four geodetic applications indicate the significant of this problem in the presence of colored and white noise in the data.     

Fracture closure modes during flowback from hydraulic fractures

Flowback analysis recently has been considered as a potential tool for assessing induced fractures through corresponding pressure analyses and rate transient analysis. In this paper, we study fracture closure mechanisms during the flowback of fracturing fluid after hydraulic fracturing treatments. Although it is known that flowback can be significantly affected by the geometry of the fractures and closure stress, there has not been any effort to understand the problem from the geomechanical point of view; rather, available studies assume that a fracture closes uniformly with constant fracture compressibility. The coupled geomechanics and fluid flow model presented in this paper help to elucidate how geostresses may affect fracturing fluid recovery under different conditions. We perform a scaling analysis to formulate the occurrence of different fracture closure modes and then use numerical analyses to verify our scaling parameters. The factors governing the flowback process include the mechanical and petrophysical properties of the rock as well as preexisting discontinuities such as natural fractures. Three different closure modes for fracture networks are described and numerically verified. The occurrence of each mode can dramatically affect fracturing fluid recovery. The role of fluid leakoff into the formation, fractures conductivity, and drawdown strategy are examined for each fracture closure scenario.     

Verifying optimality of rainfed agriculture using a stochastic model for drought occurrence

It may be paradoxical but subsistence rainfed agriculture is the predominant source of food in Sub-Saharan Africa where the production uncertainty is associated with the stochastic nature of rainfall. This paper attempts to comprehend the rationale of this situation by a mathematical approach. Considering the level of drought severity as the zero-reverting Ornstein–Uhlenbeck process, optimality of rainfed agriculture is investigated in the context of stochastic control theory. Occurrence of drought terminating growth of crops is modelled with the concept of first exit time. A stochastic control problem allowing for virtual cost of irrigation, water stress to crops, and benefits of farming is formulated with irrigation effort as the control variable. The Hamilton–Jacobi–Bellman equation governing the optimal control is studied to identify the set of cost functions optimizing rainfed agriculture in an inverse problem approach. Data and information were collected in the coastal savanna agro-ecological zone of Ghana, to identify model parameters, formulate the stochastic control problem, solve the inverse problem, and then verify optimality of rainfed agriculture. The results indicated that rainfed agriculture is not optimal when the crop is more tolerant to water stress.     

Enhanced nonlinear static analysis with the drift pushover procedure for tall buildings

In this study a new method for nonlinear static analysis based on the relative displacements of stories is proposed that is able to be implemented in a single stage analysis and considers the effects of an arbitrary number of higher modes. The method is called the extended drift pushover analysis procedure (EDPA). To define the lateral load pattern, values of the relative displacements of stories are calculated using the elastic modal analysis and the modal combination factors introduced. For determining the combination factors, six different approaches are examined. Buildings evaluated in this study consist of four special steel moment-resisting frames with 10–30 stories. Responses including relative displacements of stories, story shear forces and rotation of plastic hinges in each story are calculated using the proposed approaches in addition to modal pushover analysis and nonlinear dynamic time history analyses. The nonlinear dynamic analysis is implemented using ten consistent earthquake records that have been scaled with regard to ASCE7-10. Distribution of response errors of story shears and plastic hinge rotations show that a major part of error corresponds to the second half of the buildings studied. Thus, the mentioned responses are corrected systematically. The final results of this study show that implementing the EDPA procedure using the third approach of this research is able to effectively overcome the limitations of both the traditional and the modal pushover analyses methods and predict the seismic demands of tall buildings with good accuracy.     

Roadheader performance prediction using genetic programming (GP) and gene expression programming (GEP) techniques

Roadheading machines play a vital role in excavation operation in tunneling and mining industries notably when selective mining is required. Roadheaders are more effective in soft to medium rock formations due to a higher cutting rate in such strata. A precise prediction of machine’s performance is a crucial issue, as it has considerable effects on excavation planning, project’s cost estimation, machine specification selection as well as safety of the project. In this research, a database of machine performance and some geomechanical parameters of rock formations from Tabas coal mine project, the largest and fully mechanized coal mine in Iran, has been established, including instantaneous cutting rate (ICR), uniaxial compressive strength, Brazilian tensile strength, rock quality designation, influence of discontinuity orientation (Alpha angle) and specific energy. Afterward, the parameters were analyzed through genetic programming (GP) and gene expression programming (GEP) approaches to yield more accurate models to predict the performance of roadheaders. As statistical indices, coefficient of determination, root mean square error and variance account were used to evaluate the efficiency of the developed models. According to the obtained results, it was observed that developed models can effectively be implemented for prediction of roadheader performance. Moreover, it was concluded that performance of the GEP model is better than the GP model. A high conformity was observed between predicted and measured roadheader ICR for GEP model.