Submarine debris flow impact on pipelines — Part I: Experimental investigation

Estimating the impact forces exerted by a submarine debris flow on a pipeline is a challenge, and there is room for considerably more work to advance the state of the art. To this end, an experimental program was performed to investigate the impact on two pipeline installation scenarios: 1) suspended pipeline and 2) laid-on-seafloor pipeline. The results and observations from the experimental investigation are discussed. The definition of Reynolds number was modified for non-Newtonian fluids and an ad hoc method was developed to estimate the drag force exerted by an impact perpendicular to the pipe axis. The method may be used in prototype situations to estimate the drag force from submarine debris flow impact on pipelines. The experimental program was complemented by Computational Fluid Dynamics (CFD) analyses, the details of which are discussed in the accompanying paper.     

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.     

Late glacial and early Holocene hydroclimate variability in northwest Iran (Talesh Mountains) inferred from chironomid and pollen analysis

We reconstructed the paleohydrologic and climatic history of the Lake Neor region, NW Iran, from the end of the late glacial to the middle Holocene (15,500–7500 cal yr BP). Subfossil chironomid and pollen assemblages in a sediment core from a peatland located south of Lake Neor enabled identification of four main hydrologic phases. The period 15,500–12,700 cal yr BP was characterized by a relatively dry climate with an open landscape, suggested by the abundance of Irano-Turanian steppe plants (e.g. Amaranthaceae, Artemisia and Cousinia). Dominance of several shallow-water and semi-terrestrial chironomid taxa (e.g. Pseudosmittia, Smittia/Parasmittia and Paraphaenocladius/Parametriocnemus) during this period is indicative of lower water tables in the wetland. Between 12,700 and 11,300 cal yr BP, chironomid taxa indicate higher wetland water tables, as suggested by the presence of Zavrelia, Chironomus anthracinus/plumosus-type and Micropsectra, which are inhabitants of open-water, lacustrine areas. The open-steppe vegetation remained dominant in the watershed during this time. Increasing wetland moisture could be explained by: (1) cool summers that reduced the evaporation rate; and/or (2) a decrease in duration of the summer dry season. The period 11,300–8700 cal yr BP was characterized by lower wetland moisture, contemporaneous with a delay in the expansion of deciduous forest, suggesting persistent dry climate conditions throughout the beginning of the Holocene, which may have been related to the intensified seasonality of precipitation. Around 8700 cal yr BP, higher wetland water levels, inferred from chironomids, occurred simultaneously with the onset of regional deciduous forest expansion, probably caused by a shortening of the summer dry period. We concluded that chironomids are appropriate paleoecological proxies to investigate global and local hydrologic variability in the Middle East.     

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.     

Introducing a Comprehensive Data Reduction and Uncertainty Propagation Algorithm for U‐Th Geochronometry with Extraction Chromatography and Isotope Dilution MC‐ICP‐MS

We present an open‐source algorithm in Mathematica application (Wolfram Research) with a transparent data reduction and Monte Carlo simulation of systematic and random uncertainties for U‐Th geochronometry by multi‐collector ICP‐MS. Uranium and thorium were quantitatively separated from matrix elements through a single U/TEVA extraction chromatography step. A rigorous calibrator‐sample bracketing routine was adopted using CRM‐112A and IRMM‐035 standard solutions, doped with an IRMM‐3636a ²³³U/²³⁶U ‘double‐spike’ to account for instrumental mass bias and deviations of measured isotope ratios from certified values. The mean of ²³⁴U/²³⁸U and ²³⁰Th/²³²Th in the standard solutions varied within 0.42 and 0.25‰ (permil) of certified ratios, respectively, and were consistent with literature values within uncertainties. Based on multiple dissolutions with lithium metaborate flux fusion, U and Th concentrations in USGS BCR‐2 CRM were updated to 1739 ± 2 and 5987 ± 50 ng g^?1 (95% CI), respectively. The measurement reproducibility of our analytical technique was evaluated by analysing six aliquots of an in‐house reference material, prepared by homogenising a piece of speleothem (CC3A) from Cathedral Cave, Utah, which returned a mean age of 21483 ± 63 years (95% CI, 2.9‰). Replicate analysis of ten samples from CC3A was consistent with ages previously measured at the University of Minnesota by single‐collector ICP‐MS within uncertainties.     

Multi-time-scale analysis of hydrological drought forecasting using support vector regression (SVR) and artificial neural networks (ANN)

In this study, the preprocessing of the gamma test was used to select the appropriate input combination into two models including the support vector regression (SVR) model and artificial neural networks (ANNs) to predict the stream flow drought index (SDI) of different timescales (i.e., 3, 6, 9, 12, and 24 months) in Latian watershed, Iran, which is one of the most important sources of water for the large metropolitan Tehran. The variables used included SDI _t , SDI _t ? 1, SDI _t ? 2, SDI _t ? 3, and SDI _t ? 4 monthly delays. Two variables including SDI _t and SDI _t ? 1 with lower gamma values were identified as the most optimal combination of variables in all drought timescales. The results showed that the gamma test was able to correctly identify the right combination for the forecasting of 6, 9, and 12 months SDI using the ANN model. Also, the gamma test was considered in selecting the appropriate inputs for identifying the values of 9, 12, and 24 months SDI in SVR. The support vector machine approach showed a better efficiency in the forecast of long-term droughts compared to the artificial neural network. In total, among forecasts made for 30 scenarios, the support vector machine model only in scenario 3 of SDI₃, scenario 1 of SDI₆, and scenarios 2 and 3 of SDI₂₄ represented poorer efficiency compared to the artificial neural network (MLP layer), but in other scenarios, the results of SVR had better efficiency.     

Watershed classification by remote sensing indices: A fuzzy c-means clustering approach

Determining the relatively similar hydrological properties of the watersheds is very crucial in order to readily classify them for management practices such as flood and soil erosion control. This study aimed to identify homogeneous hydrological watersheds using remote sensing data in western Iran. To achieve this goal, remote sensing indices including SAVI, LAI, NDMI, NDVI and snow cover, were extracted from MODIS data over the period 2000 to 2015. Then, a fuzzy method was used to clustering the watersheds based on the extracted indices. A fuzzy c-mean (FCM) algorithm enabled to classify 38 watersheds in three homogeneous groups. The optimal number of clusters was determined through evaluation of partition coefficient, partition entropy function and trial and error. The results indicated three homogeneous regions identified by the fuzzy c-mean clustering and remote sensing product which are consistent with the variations of topography and climate of the study area. Inherently, the grouped watersheds have similar hydrological properties and are likely to need similar management considerations and measures.     

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.     

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.     

Applying wavelet transformation and artificial neural networks to develop forecasting-based reservoir operating rule curves

ABSTRACT

In order to provide more accurate reservoir-operating policies, this study attempts to implement effective monthly forecasting models. Seven inflow forecasting schemes, applying discrete wavelet transformation and artificial neural networks are proposed and provided to forecast the monthly inflow of Dez Reservoir. Based on some different performance indicators the best scheme is achieved comparing to the observed data. The best forecasting model is coupled with a simulation-optimization framework, in which the performance of five different reservoir rule curves can be compared. Three applied rules are based on conventional Standard operation policy, Regression rules, and Hedging rule, and two others are forecasting-based regression and hedging rules. The results indicate that forecasting-based operating rule curves are superior to the conventional rules if the forecasting scheme provides results accurately. Moreover, it can be concluded that the time series decomposition of the observed data enhances the accuracy of the forecasting results efficiently.