Performance Forecasting Research Articles

Gradient-based Pareto optimal history matching for noisy data of multiple types

The advantages of the simultaneous integration of production and time-lapse seismic data for history matching have been demonstrated in a number of previous studies. Production data provide accurate observations at particular spatial locations (wells), while seismic data enable global, though filtered/noisy, estimates of state variables. In this work, we present an efficient computational tool for bi-objective history matching, in which data misfits for both production and seismic measurements are minimized using an adjoint-gradient approach. This enables us to obtain a set of Pareto optimal solutions defining the optimal trade-off between production and seismic data misfits (which are, to some extent, conflicting). The impact of noise structure and noise level on Pareto optimal solutions is investigated in detail. We discuss the existence of the “best” trade-off solution, or least-conflicting posterior model, which corresponds to the history-matched model that is expected to provide the least-conflicting forecast of future reservoir performance. The overall framework is successfully applied in 2D and 3D compositional simulation problems to provide a single least-conflicting posterior model and, for the 2D case, multiple samples from the posterior distribution using the randomized maximum likelihood method.

Numerical simulation of propeller exciting force induced by milling-shape ice

On the basis of the Computational Fluid Dynamics technique (CFD) combined with the overlap grid method, this paper establishes a numerical simulation method to study the problem of ice-propeller interaction in viscous flow and carries out a simulation forecast of the hydrodynamic performance of an ice-class propeller and flow characteristics when in the proximity of milling-shape ice (i.e., an ice block with a groove cut by a high-speed revolving propeller). We use a trimmed mesh in the entire calculation domain and use the overlap grid method to transfer information between the domains of propeller rotation calculation and ice-surface computing. The grid is refined in the narrow gap between the ice and propeller to ensure the accuracy of the flow field. Comparison with the results of the experiment reveals that the error of the hydrodynamic performance is within 5%. This confirms the feasibility of the calculation method. In this paper, we calculate the exciting force of the propeller, analyze the time domain of the exciting force, and obtain the curve of the frequency domain using a Fourier transform of the time-domain curve of the exciting force. The existence of milling-shape ice before the propeller can greatly disturb the wake flow field. Unlike in open water, the propeller bearing capacity shows a downward trend in three stages, and fluctuating pressure is more disordered near the ice.

Study of a Pump-as-Turbine (PaT) speed control for a Water Distribution Network (WDN) in South-Tyrol subjected to high variable water flow rates

The development of renewable energy technologies for producing clean energy has more and more become a priority worldwide. Research activities have not just to target the technological improvement of such systems, but they have also to consider their market deployment. In such a scenario, hydraulic machines, in particular Pumps-as-Turbines (PaTs), can play a key role in energy recovery applications. One of the main open issues of PaTs is the performance forecast in turbine mode, due to the lack of data from manufacturers, and their use in some applications with high flow rate and pressure variability, especially at part-load operating conditions like in energy recovery applications within Water Distribution Networks (WDNs). In this work, a MATLAB® Simulink model is developed for simulating a branch of the WDN located in Laives (South-Tyrol), where specific PaTs have been selected and used to substitute Pressure Reducing Valves (PRVs). A speed control by means of an inverter is performed due to the high variability of the flow rate inside the grid branch, allowing the machines to operate at their Best Efficiency Point (BEP). A preliminary analysis showed that it is possible to increase the energy production of about 23% with respect to a constant-speed machine, leading to a significant decrease of the PayBack Period (PBP).

Analysis of Factors Affecting the Compressive Strength of Cement Stabilized Gravel

The compressive strength of cement stabilized gravel is an important parameter of pavement base design and performance evaluation. In this study, the parameter selection of the compressive strength of cement stabilized macadam as the research direction, by collecting both the compressive strength of cement stabilized macadam mixture test data, establishing database of strength, obtain various parameters represent value under different reliability, analysis of specimen forming method, type of aggregate gradation and different influence factors such as the relationship between the compressive strength of the cement stable macadam mixture, using the mathematical regression method, set up under the condition of different curing ages, cement dose the prediction equation of intensity of verified the established logarithm model has higher fitting precision and can provide for pavement structure design and performance forecast value basis.

Parallel algorithm portfolios with performance forecasting

ABSTRACTWe propose a novel algorithm portfolio model that incorporates time series forecasting techniques to predict online the performance of its constituent algorithms. The predictions are used to allocate computational resources to the algorithms, accordingly. The proposed model is demonstrated on parallel algorithm portfolios consisting of three popular metaheuristics, namely tabu search, variable neighbourhood search, and multistart local search. Moving average and exponential smoothing techniques are employed for forecasting purposes. A challenging combinatorial problem, namely the detection of circulant weighing matrices, is selected as the testbed for the analysis of the proposed approach. Experimental evidence and statistical analysis provide insight on the performance of the proposed algorithms and reveal the benefits of using forecasting techniques for resource allocation in algorithm portfolios.

Numerical Investigation of Cavitating Flows with Liquid Degassing

Cavitation is a phenomenon of formation of bubbles (cavities) in liquid as a result of pressure drop. Cavitation plays an important role in a wide range of applications. For example, cavitation is one of the key problems of design and manufacturing of pumps, hydraulic turbines, ship’s propellers, etc. Special attention is paid to cavitation erosion and to performance degradation of hydraulic devices (noise, fluctuation of the mass flow rate, etc.) caused by formation of a two phase system with an increased compressibility. One more important problem accompanying cavitation is liquid degassing due to diffusion of the dissolved gas into the cavities. Determination of the degassed air content is important for the performance forecast of devices. Mathematical models of the degassing in cavitating flows are not yet developed and presented in the literature satisfactorily. Therefore, development of a model for simultaneous cavitation and liquid degassing is an important fundamental and applied task. To validate the algorithm simulations of unsteady flow of a cavitating liquid in a channel have been conducted. The obtained results are in satisfactory agreement with the experimental data and demonstrate the efficiency and robustness of the formulated model and the algorithm.

Genetic and phenotypic markers for successful athletic performance forecast

In the modern research and practical provisions for the children’s and adolescent sports, due emphasis will be made on a timely detection of the physical activity limiting factors and prudent initiatives to remove or mitigate these factors with the relevant correction tools being applied to attain the highest competitive performance standards with no detriment for the athlete’s health.
 Based on the study of the 5HTT gene polymorphisms in junior football and ice hockey players, about 25% and 20% of the football and ice hockey players, respectively, were diagnosed with the unfavourable genetic type predisposed to indirect aggression that may be of negative effect on their training and competitive activities.
 The functional deviations identified by the study dictate the need in a reasonable pharmacological intervention to optimize the metabolic processes in the CNS and VNS and help maintain the high physical and mental workability rates.

Genetic and phenotypic markers for successful athletic performance forecast

Genetic and phenotypic markers for successful athletic performance forecast

A Fusion Approach for Exploring the Key Factors of Corporate Governance on Corporate Social Responsibility Performance

It is widely recognized that a firm’s well-established corporate governance (CG) has a considerable impact on its corporate social responsibility (CSR) performance. How to determine the main trigger among CG’s indicators for strengthening CSR performance is thus an urgent and complicated task due to its (i.e., CSR) multi-dimensional and numerous perspectives. In order to solve this critical problem, the study breaks down CSR into four dimensions and further examines the impact of CG’s indicators on each CSR dimension by joint utilization of rough set theory (RST) and decision tree (DT). By doing so, users can realize which one CG indicator is the most essential to CSR performance. Managers can take the results as a reference to allocate valuable and scarce resources to the right place so as to enhance CSR performance in the future. To solidify our research finding, we transform the CSR forecasting model selection into a multiple criteria decision making (MCDM) task and execute a MCDM algorithm. By implementing the MCDM algorithm, users can achieve a much more reliable and consensus decision in today’s highly turbulent economic environment. The proposed mechanism, examined by real cases, is a promising alternative for CSR performance forecasting.

Safety leading indicators for construction sites: A machine learning approach

Abstract The construction industry is one of the most dangerous industries in many countries. To improve the situation, senior managers overseeing portfolios of construction projects need to understand the safety risk levels of their projects so that interventions can be implemented proactively. Safety leading indicators is one way to flag sites that are of higher risk. However, there is a lack of validated leading indicators that can reliably classify sites according to their safety risk levels. On the other hand, despite the success of machine learning (ML) approaches in other domains, it is not widely utilized in the construction industry, especially in the development of safety leading indicators. This paper presents a ML approach to developing leading indicators that classify sites in accordance to their safety risk in construction projects. This study was guided by the industry-recognized Cross-Industry Standard Process for Data Mining (CRISP-DM) framework and the key types of data used include safety inspection records, accident cases and project-related data. These data were obtained from a large contractor in Singapore and the data were accumulated from year 2010 to 2016. Out of thirty-three input variables (also known as features or independent variables), 13 input variables were selected using a combination of Boruta feature selection technique and decision tree. Of the 13 selected input variables, six of them are project-related (project type, project ownership, contract sum, percent completed, magnitude of delay and project manpower) and seven of them are items in the contractor's safety inspection checklists (crane/lifting operations, scaffold, mechanical-elevated working platform, falling hazards/openings, environmental management, good practices and weighted safety inspection score). Five popular ML algorithms were then used to train models for prediction of accident occurrence and severity. During validation, random forest (RF) provided the best prediction performance with an accuracy of 0.78 and has achieved a substantial strength of agreement with Weighted-Kappa Statistics of 0.70. Comparing with similar studies, this result is promising. The prediction (i.e. the output variable) provided by the RF model can be used as a safety leading indicator of the risk level of a site. It is recommended that the predictive RF model be deployed in construction organizations, especially large public and private developers, contractors and industry associations, to provide monthly forecast of project safety performance so that pre-emptive inspections and interventions can be implemented in a more targeted manner.

THEORETICAL AND EXPERIMENTAL STUDY ON STRESS-DEPENDENCY OF OIL–WATER RELATIVE PERMEABILITY IN FRACTAL POROUS MEDIA

The coupled flow deformation behavior in the porous media has drawn tremendous attention in various scientific and engineering fields. It is reported that the porous media will be compressed and relative permeability in porous media will be changed as the effective stress increases. However, previous studies provided contradictory evidence for the stress-dependent irreducible water saturation and stress-dependent relative permeability. Until now, appropriate stress-dependent relative permeability curve for two-phase flow through porous media remains unclear. The goal of this work was to theoretically and experimentally study the stress-dependent relative permeability. Laboratory sample flooding tests were conducted to measure two-phase relative permeability in porous media under changing effective stress, and a corresponding theoretical model of stress-dependent relative permeability was derived to interpret the experimental results. The predictions from the proposed analytical model exhibited similar variation trends as the experimental data, which verified the theoretical model. Though the results for the stress-dependent relative permeability from previous studies are different, or even opposite, our proposed model with different conditions can provide explanations to these different results. This work provides a comprehensive experimental and theoretical study of stress-dependent relative permeability in porous media, which is beneficial to accurate performance forecasts for the coupled flow deformation behavior in porous media.

Forecasting performance of regional innovation systems using semantic-based genetic programming with local search optimizer

Innovation performance of regional innovation systems can serve as an important tool for policymaking to identify best practices and provide aid to regions in need. Accurate forecasting of regional innovation performance plays a critical role in the implementation of policies intended to support innovation because it can be used to simulate the effects of actions and strategies. However, innovation is a complex and dynamic socio-economic phenomenon. Moreover, patterns in regional innovation structures are becoming increasingly diverse and non-linear. Therefore, to develop an accurate forecasting tool for this problem represents a challenge for optimization methods. The main aim of the paper is to develop a model based on a variant of genetic programming to address the regional innovation performance forecasting problem. Using the historical data related to regional knowledge base and competitiveness, the model should accurately and effectively predict a variety of innovation outputs, including patent counts, technological and non-technological innovation activity and economic effects of innovations. We show that the proposed model outperforms state-of-the-art machine learning methods.

Albanian Challenges Towards an Efficient Pension System

Pensions' reform in most Western, Central and Eastern European countries is one of the most important topics relevant for their future development. The main objective of this study is to assist in predicting the future of the pension scheme in Albania through actuarial projections related to demographic structure, unemployment, number of contributors and beneficiaries. Through this study, we intended to predict what would be the financial effect of two options. First, we investigate the likely outcome if Albania continues with the same scheme as currently used. Second, we investigate the likely outcome when a new contributory scheme is being implemented. The methodology used for implementing the actuarial model is based on the construction of the population projections using the RUP system (Rural Urban Projection), a system developed by the Census Bureau of the United States of America, while the economic performance forecasts, i.e.: GDP, inflation rate, unemployment rate or expected indexation of wages and pensions, are be sourced by the Albanian Ministry of Finance and the International Monetary Fund. The study concludes and recommends some of the steps for reforming the pension scheme in Albania based on the experience of other countries and the likely financial effect to the state budget in case of the implementation of the new scheme.

A macroeconometric model for trade policy evaluation: evidence from Pakistan

PurposeThe purpose of the study is to develop a macroeconometric model for evaluation of trade policies and forecasting of trade performance of Pakistan with different regions or group of countries.Design/methodology/approachThese regions or group of countries are Organization of Islamic Cooperation, Organization of Economic Cooperation and Development, Association of Southeast Asian Nations, South Asian Association for Regional Cooperation and the rest of the world. A macroeconometric model containing 15 behavioral equations and eight identities.FindingsCointegration results suggest that there exist long-run relationships among variables of all behavioral equations. Additionally, results of different policy shocks based on unit value of export (export price), unit value of import (import price), exchange rate, foreign direct investment, interest rate and foreign exchange reserve suggest that the model is useful for economic planning to sustain growth performance of Pakistan.Originality/valueIn this study, the authors develop for the first time ever a macroeconometric model for the evaluation and forecasting of regional trade policy and performance for Pakistan.

Performance forecasting for polymer flooding in heavy oil reservoirs

As a supply for future fuel and energy demand, 95% of the bitumen deposits in North America are expected to become a major source. The Steam Assisted Gravity Drainage (SAGD) provides more efficient recovery of unconventional oil resources, such as heavy oil and bitumen, as compared to the other thermal recovery methods. The drawback associated with SAGD or other thermal methods is that they are economically non-profitable when applied to the deep and thin reservoirs. Environmental concerns related to land, water, and air also hinder the application of the aforementioned methods. These issues have provoked reservoir engineers to employ a remarkable alternate such as polymer flooding recovery technique in heavy oil reservoirs. Quick and practical decision-making process in presence of uncertainty-based reservoir development scenarios is a notable stimuli for reservoir management teams to find substitute modeling techniques for future performance forecasting of heavy oil reservoirs. Cognitive data-driven analytics, including artificial and computational intelligence techniques, statistical analyses, and data-mining practices, offers an attractive alternate especially in presence of high-dimensional data space and predictive modeling of an extremely nonlinear system. This study utilizes an extensive data set from the half-century review of laboratory to field scales polymer flooding in heavy oil reservoirs provided by Saboorian-Jooybari et al. (2015) and (2016). The exploratory data analysis is implemented to construct a comprehensive training data set from polymer flooding experimental and field data, which involves various attributes describing characteristics associated with reservoir heterogeneities and pertinent operating parameters. Demonstrated results imply that this advanced data-driven modeling technique has a great potential to be integrated into all reservoir development tools for future performance predictions of the underlying processes.

Integrated news mining technique and AI-based mechanism for corporate performance forecasting

The deterioration in a corporation's profitability not only threatens its interests and sustainable development but also causes tremendous losses to other investors. Hence, constructing an effective pre-warning model for performance forecasting is an urgent requirement. Most previous studies only analyzed monetary-based ratios, but merely considering such ratios does not depict the full perspective of a corporation's business conditions. This study thus extends monetary-based ratios to non-monetary-based ratios and aggregates them through the analytic network process (ANP) with a risk-adjusted strategy to establish performance ranks of corporations. Analyzing a corporation's business relationships can help it to react to changes in the market and improve profit margins, as it draws upon such relationship networks for the transfer of scarce resources and knowledge. We believe that no current study adopts such a method to construct a forecasting model. To fill this gap in the literature, this study implements the social network (SN) technique to examine a corporation's competitive edge from seemingly noisy big media data, which are subsequently fed into an artificial intelligence (AI)-based technique to construct the model. The introduced model, examined through real-life cases under numerous conditions, offers a promising alternative for performance forecasting.

Flow behavior analysis of two-phase (gas/water) flowback and early-time production from hydraulically-fractured shale gas wells using a hybrid numerical/analytical model

In recent years, quantitative analysis of flowback and early-time production data for interpretation of hydraulic fracture properties in shale has received significant attention. With high-resolution phase rates and flowing pressures, analysis of flowback and early-time production data provides an early opportunity to determine key parameters for well performance forecasting. The main purpose of this paper is to quantify flow characteristics associated with two-phase flowback and early-time production of shale gas wells exhibiting both planar fractures and a complex fracture network using a rigorous and efficient flow model.To study flow characteristics of this complex system, a hybrid numerical/analytical model is developed in which matrix and hydraulic fracture flow is accounted for. The finite-difference method is used to numerically model unsteady-state two-phase fracture flow. The analytical matrix flow model, derived using the line-source function, is dynamically coupled with the fracture flow model by imposing continuity of pressure and flux on the fracture surface. The main advantage of the hybrid solution over more conventional numerical simulation is reduced model setup and run time, without loss of the important physics.Detailed flow regime analysis reveals that the gas/water flowback and early-production of a horizontal well with a planar fracture geometry may the following complex sequence of flow-regime: 1) radial flow of water in the fracture; 2) a first transient linear flow of water in the fracture; 3) a second transient linear flow of water in the fracture; 4) boundary-dominated flow of water in the fracture and gas linear flow in matrix. Water primarily depletes in the first three flow periods. Development of the water boundary-dominated flow and gas linear flow indicates the system indicate that the system is at the end of flowback period and proceeds early-time production. Specially, the second water linear flow period develops when both gas and water production are dominating the system, and is caused by the pressure maintenance associated with gas flux from matrix. A complex fracture network with the same total fracture length as a planar fracture system will exhibit the same sequence of flow regimes. Due to the accelerated water drainage caused by multiple contact points between fractures and the wellbore in complex fracture network, the system also exhibits a shortened second water transient linear flow period and earlier water boundary-dominated flow.Finally, the new model is also applied to match flowback data obtained from a multi-fractured horizontal well completed in the Marcellus Shale. The results demonstrate the practical application of the new model for deriving reservoir/fracture properties from flowback data and for forecasting.

Forecast of thermal harvesting performance under multi-parameter interaction with response surface methodology

Benefitting from transformation optics, the design of thermal harvesting devices by manipulating heat flux has aroused considerable interest owing to its potential application in heat storage and the fabrication of highly efficient and reliable solar cells and thermal collectors. However, studies on harvesting performance under different conditions achieved by the use of different design parameters are lacking. In this study, we focus on forecasting the harvesting performances of 2D thermal concentrating schemes with variational design parameters, including the ratios of conductivities and radii, wedge angles, and temperature differences. In order to evaluate the multi-parametric harvesting performance, an expression of efficient harvesting entropy was proposed based on the entropy generation distributions of thermal harvesting schemes. Response surface methodology (RSM) was employed to achieve the regression model of efficient harvesting entropy under different conditions. The accuracy of the forecast was verified by analysis of variance (ANOVA) and five confirmation tests. Moreover, the effects of interaction influences on efficient harvesting entropy of design parameters were described by 3D response surface. In general, this study indicates that a harvesting scheme with a higher ratio of conductivities and a smaller wedge angle and ratio of radii under a specified temperature gradient can provide a better concentrating performance. With the theoretical forecast of the harvesting performance, a satisfactory thermal harvesting device can be built under homologous conditions.

The effects of CEOs’ Linguistic Abstraction on Financial Analysts’ Performance Forecasts

Based on Construal Level Theory, we hypothesize that CEOs who speak abstractly are more likely to be perceived as visionary than CEOs who speak concretely, resulting in more favorable perceptions a...

Internal Operating Environment, Forecasting Techniques and Accuracy of Performance Forecasting

This article examines the influence of the internal operating environment (IOE) on the accuracy of performance forecasting (APF) in large manufacturing firms (LMFs) in Kenya. The objective of this project was to assess if any aspects of the IOE influenced measures of APF. APF, in manufacturing operations, is seldom derived accurately due to internal conflicts of interest. However, since LMFs hire skilled personnel, this study assumes that the qualified forecasting staff ensures accuracy in forecasts when preparing future budgets. IOE is said to influence the behavior of operations resulting in either adverse or favorable organizational performance. This study identified IOE factors that impact firm performance and tested these against measures of APF. Regression analysis was applied using data collected through a structured questionnaire administered among randomly selected LMFs. Results indicated that there was no evidence that IOE had an influence on APF, but it had a moderating effect on the relationship between the objective forecasting technique and APF through return on sales (ROS).