An enriched FEM technique for modeling hydraulically driven cohesive fracture propagation in impermeable media with frictional natural faults: Numerical and experimental investigations

In this paper, an enriched finite element technique is presented to simulate the mechanism of interaction between the hydraulic fracturing and frictional natural fault in impermeable media. The technique allows modeling the discontinuities independent of the finite element mesh by introducing additional DOFs. The coupled equilibrium and flow continuity equations are solved using a staggered Newton solution strategy, and an algorithm is proposed on the basis of fixed‐point iteration concept to impose the flow condition at the hydro‐fracture mouth. The cohesive crack model is employed to introduce the nonlinear fracturing process occurring ahead of the hydro‐fracture tip. Frictional contact is modeled along the natural fault using the penalty method within the framework of plasticity theory of friction. Moreover, an experimental investigation is carried out to perform the hydraulic fracturing experimental test in fractured media under plane strain condition. The results of several numerical and experimental simulations are presented to verify the accuracy and robustness of the proposed computational algorithm as well as to investigate the mechanisms of interaction between the hydraulically driven fracture and frictional natural fault. Copyright © 2015 John Wiley & Sons, Ltd.     

Emerging technologies for identification of disinfection byproducts: GC/FT-ICR MS characterization of solvent artifacts

Water samples from a local water treatment plant were analyzed, using gas chromatography Fourier transform ion cyclotron resonance mass spectrometry (GC/FT-ICR MS), to identify potential disinfection byproducts (DBPs). Both liquid-liquid extraction (LLE) and solid-phase microextraction (SPME) techniques were used for sample preparation prior to GC/MS analyses. Based on the averaged mass measurement accuracy (MMA) of better than five parts-per-million (<5 ppm), multiple solvent artifacts were identified. It is shown that solventless SPME can be utilized to reduce potential interferences from solvent stabilizers. Six DBPs were detected and their molecular compositions were assigned at a high level of confidence. At the ppb concentration ranges and in the broadband mass spectral detection mode, internally calibrated mass spectra provided concurrent high resolution (resolving power M/deltaM50% > 30,000 at m/z values -110) and MMA of better than one part-per-million (MMA < 1 ppm). The use of thermochemical data, such as proton affinities, as a complementary tool to enhance analytical resolution is also demonstrated.     

Two-stage Robust Network Design with Exponential Scenarios

We study two-stage robust variants of combinatorial optimization problems on undirected graphs, like Steiner tree, Steiner forest, and uncapacitated facility location. Robust optimization problems, previously studied by Dhamdhere et al. (Proc. of 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS’05), pp. 367–378, 2005), Golovin et al. (Proc. of the 23rd Annual Symposium on Theoretical Aspects of Computer Science (STACS), 2006), and Feige et al. (Proc. of the 12th International Integer Programming and Combinatorial Optimization Conference, pp. 439–453, 2007), are two-stage planning problems in which the requirements are revealed after some decisions are taken in Stage 1. One has to then complete the solution, at a higher cost, to meet the given requirements. In the robust k-Steiner tree problem, for example, one buys some edges in Stage 1. Then k terminals are revealed in Stage 2 and one has to buy more edges, at a higher cost, to complete the Stage 1 solution to build a Steiner tree on these terminals. The objective is to minimize the total cost under the worst-case scenario. In this paper, we focus on the case of exponentially many scenarios given implicitly. A scenario consists of any subset of k terminals (for k-Steiner tree), or any subset of k terminal-pairs (for k-Steiner forest), or any subset of k clients (for facility location). Feige et al. (Proc. of the 12th International Integer Programming and Combinatorial Optimization Conference, pp. 439–453, 2007) give an LP-based general framework for approximation algorithms for a class of two stage robust problems. Their framework cannot be used for network design problems like k-Steiner tree (see later elaboration). Their framework can be used for the robust facility location problem, but gives only a logarithmic approximation. We present the first constant-factor approximation algorithms for the robust k-Steiner tree (with exponential number of scenarios) and robust uncapacitated facility location problems. Our algorithms are combinatorial and are based on guessing the optimum cost and clustering to aggregate nearby vertices. For the robust k-Steiner forest problem on trees and with uniform multiplicative increase factor for Stage 2 (also known as inflation), we present a constant approximation. We show APX-hardness of the robust min-cut problem (even with singleton-set scenarios), resolving an open question of (Dhamdhere et al. in Proc. of 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS’05), pp. 367–378, 2005) and (Golovin et al. in Proc. of the 23rd Annual Symposium on Theoretical Aspects of Computer Science (STACS), 2006).     

A local linear radial basis function neural network for financial time-series forecasting

In this paper a Local Linear Radial Basis Function Neural Network (LLRBFN) is presented. The difference between the proposed neural network and the conventional Radial Basis Function Neural Network (RBFN) is connection weights between the hidden layer and the output layer which are replaced by a local linear model in the LLRBFN. A modified Particle Swarm Optimization (PSO) with hunter particles is introduced for training the LLRBFN. The proposed methods have been applied for prediction of financial time-series and the result shows the feasibility and effectiveness.     

On the use of the RMR system for estimation of rock mass deformation modulus

Estimation of rock mass deformation modulus is the subject of many studies in rock engineering research work. Although numerous predictive models have been developed for the estimation of the deformation modulus, they cannot be generalized for other sites because of inadequate accuracy. Furthermore, it is very valuable that the predictive models involve some accessible input parameters. The rock mass rating (RMR) is a well-known geomechanical parameter, which is usually determined to describe the quality of rock mass in rock engineering projects. In this study, five parameter ratings of the RMR classification system are used to predict the deformation modulus of rock mass in the abutment of the Gotvand earth dam. Statistical analysis and an artificial neural network are employed to present two new predictive models. Finally, probabilistic analysis is used to predict the rock mass deformation modulus, which overcomes the low accuracy caused by the inherent uncertainty in prediction. The results indicated that the parameter ratings used in the RMR classification system can predict the rock mass deformation modulus with a satisfactory correlation. However, the parameters don’t have the same influence on the rock mass deformability with the joint condition and the groundwater as the major and minor influencing parameters, respectively.     

PALAEO‐EXPOSURE SURFACES IN THE APTIAN DARIYAN FORMATION,OFFSHORE SW IRAN: GEOCHEMISTRY AND RESERVOIR IMPLICATIONS

Palaeo‐exposure surfaces within and at the top of the carbonate‐dominated Aptian Dariyan Formation have been poorly studied in the Iranian sector of the Persian Gulf. This paper presents an integrated sedimentological and geochemical study of the Dariyan Formation at four oil and gas fields located in the western, central and eastern parts of the Gulf. Facies stacking patterns in general indicate shallowing‐upwards trends toward the exposure surfaces, which are interpreted to correspond to unconformities. The Dariyan Formation in the study area is divided into upper and lower carbonate units by a deep‐water, high‐gamma shale‐marl interval. At fields in the western and central Gulf, significant diagenetic changes were recorded in the top of the upper carbonate unit, including meteoric dissolution and cementation, brecciation and paleosol formation. An exposure surface is also present at the top of the lower carbonate unit in all the fields in the study area, and is associated with meteoric dissolution and cementation of grain‐dominated facies. Age calibration of studied intervals was carried out using microfossil assemblages including benthic and planktonic foraminifera. Negative excursions of both δ¹⁸O (?10‰ VPDB) and δ¹³C (?0.66‰ VPDB) were recorded in weathered intervals located below the unconformity surfaces. A sequence stratigraphic framework for the Dariyan Formation was established by integrating sedimentological, palaeontological and geochemical data. The δ¹³C curve for the formation in the Iranian sector of the Persian Gulf can be correlated with the reference curve for the northern Neotethys and used as a basis for regional stratigraphic correlation. Where the top‐Aptian unconformity is present, it has resulted in an enhancement of the reservoir characteristics of the underlying carbonate succession. Accordingly, the best reservoir zones in the Dariyan Formation occur in the upper parts of the lower and upper carbonate units which are bounded above by significant palaeo‐exposure surfaces.     

CARBONATE RESERVOIR ROCKS AT GIANT OIL AND GAS FIELDS IN SW IRAN AND THE ADJACENT OFFSHORE: A REVIEW OF STRATIGRAPHIC OCCURRENCE AND PORO‐PERM CHARACTERISTICS

SW Iran and the adjacent offshore are prolific petroleum‐producing areas with very large proven oil and gas reserves and the potential for significant new discoveries. Most of the oil and gas so far discovered is present in carbonate reservoir rocks in the Dehram, Khami and Bangestan Groups and the Asmari Formation, with smaller volumes in the Dashtak, Neyriz, Najmeh, Gurpi, Pabdeh, Jahrum, Shahbazan, Razak and Mishan (Guri Member) Formations. The Permo‐Triassic Dehram Group carbonates produce non‐associated gas and condensate in Fars Province and the nearby offshore. The Jurassic – Lower Cretaceous Khami Group carbonates are an important producing reservoir at a number of offshore fields and in the southern Dezful Embayment, and are prospective for future exploration. Much of Iran's crude oil is produced from the Oligo‐Miocene Asmari Formation and the mid‐Cretaceous Sarvak Formation of the Bangestan Group in the Dezful Embayment. This review paper is based on data from 115 reservoir units at 60 oil‐ and gasfields in SW Iran and the adjacent offshore. It demonstrates that the main carbonate reservoir units vary from one‐another significantly, depending on the particular sedimentary and diagenetic history. Ooidal‐grainstones and rudist‐ and Lithocodium‐bearing carbonate facies form the most important reservoir facies, and producing units are commonly dolomitised, karstified and fractured. In general, reservoir rocks in the study area can be classified into six major types: grainstones; reefal carbonates; karstified, dolomitised and fractured carbonates; and sandstones. The stratigraphic distribution of these reservoir rocks was principally controlled by the palaeoclimatic conditions existing at the time of deposition. A comparative reservoir analysis based on core data shows that dolomitised and/or fractured, grain‐dominated carbonates in the Dehram Group, Lower Khami Group and Asmari Formation typically have better reservoir qualities than the Cretaceous limestones in the Upper Khami and Bangestan Groups.     

A fuzzy irregular cellular automata-based method for the vertex colouring problem

ABSTRACT

Vertex colouring is among the most important problems in graph theory which has been widely applied across different real-world problems. In vertex colouring problem (VCP), the goal is to assign a distinct colour to each vertex of the graph in such a way that no two adjacent vertices have the same colour. This paper presents a fuzzy irregular cellular automaton (FICA) for finding a near-optimal solution for the VCP. FICA is an extension fuzzy cellular automaton (FCA) in which the cells of the automaton can be arranged in an irregular structure. The aim of the proposed method is to reap the benefits of both FCA and irregular cellular automata while minimising their drawbacks. To evaluate the proposed method, various computer simulations have been conducted on a variety of graphs. The results suggest that the proposed method is able to achieve better results in terms of the minimum number of required colours and the execution time of the algorithm, compared to other peer algorithms.     

A wrapper‐based feature selection for improving performance of intrusion detection systems

Along with expansion in using of Internet and computer networks, the privacy, integrity, and access to digital resources have been faced with permanent risks. Due to the unpredictable behavior of network, the nonlinear nature of intrusion attempts, and the vast number of features in the problem environment, intrusion detection system (IDS) is regarded as the main problem in the security of computer networks. A feature selection technique helps to reduce complexity in terms of both the executive load and the storage by selecting the optimal subset of features. The purpose of this study is to identify important and key features in building an IDS. To improve the performance of IDS, this paper proposes an IDS that its features are optimally selected using a new hybrid method based on fruit fly algorithm (FFA) and ant lion optimizer (ALO) algorithm. The simulation results on the dataset KDD Cup99, NSL‐KDD, and UNSW‐NB15 have shown that the FFA–ALO has an acceptable performance according to the evaluation criteria such as accuracy and sensitivity than previous approaches.