An Elastomer Additive Improving Elastic Properties of Heavy Weight Oil Well Cement: A Laboratory Study

The use of elastomer additives to solve the problems in oil well cementing has been investigated in recent years by several research groups in the petroleum industry. This study includes the laboratory examination of the effect of elastomer additives on the physical properties of heavy-weight oil well cement. In the research process, a candidate well is selected and the properties of the cement slurry used in a problematic section of the well are tested in the laboratory. Then, elastomer additives are added as an elastic agent and the improvements in the cement slurry and stone properties are studied. This article discusses the problems associated with the conventional heavy-weight oil well cement used in the candidate well and reports the detail of the improvements in cement properties obtained by adding an elastomer additive to the cement slurry formulation as an elastic agent. These properties include cement slurry rheological properties, free water, fluid loss, thickening time, cement stone elasticity properties, and compressive strength. The elastomer additive increases the yield point and plastic viscosity, but it decreases the free water and fluid loss of cement slurry. In addition, the cement stone compressive strength decreases; however, there is an optimum concentration of the elastomer additive at which the maximum compressive strength is reached. Moreover, the elasticity properties of the cement stone are improved and a lower value for the Young’s modulus and a higher value for the Poisson’s ratio are achieved. The theories supporting the results are discussed in the discussion section. The results of this study can be used to optimize the cement slurry design in any given set of conditions.</span

Using an Elastic, Expandable Sealant System for Zonal Isolation of Maroon Wells: a Laboratory Study

An oil and gas well cementing in Gachsaran formation, where sustained annular pressure has been reported in many wells, presents a big challenge in Maroon field. The main challenges are preventing gas migration and achieving zonal isolation using a competent cement sealant system which is able to withstand downhole stresses and high temperatures during production cycles. Unlike conventional cement systems, properties, such as, high Poisson’s ratio and low Young’s modulus compared to that of the rock were optimized in the new system to achieve mechanical resistance and durability. The use of elastic-expandable additives to solve problems in oil well cementing has been investigated in recent years by several research groups in the petroleum industry. This study includes the laboratory examination of the effect of an elastic-expandable additive on the physical properties of a new cement sealant system. In the research process, a candidate well was selected and the properties of the used cement slurry in a problematic section of the well were evaluated in the laboratory. Then, the elastic-expandable additive was added as an elastic agent and the improvements in the cement slurry and stone properties were studied. This article discusses the problems associated with the conventional cement used in the candidate well and gives the detail of the improvements in cement properties obtained by adding the elastic-expandable additive to the cement slurry formulation as an elastic agent. The elastic-expandable additive increases the Poisson’s ratio and expansion set cement, but it decreases the Young’s modulus and fluid loss of the cement slurry. In addition, to prevent gas migration and achieve zonal isolation, there is an optimum concentration of the elastic-expandable additive at which the maximum compressive strength is reached. The results of this study can be used to optimize the cement slurry design in any given set of conditions

Computer-aided optimal open pit design with variable slope angles

The use of open pit mining has increased to extract large and low grade deposits with the growth in demand for raw materials, with the advances in mining technology and with the depletion of high grade and readily accessible orebodies. Development and extraction of minerals by this method is a complex operation that may extend over several decades and require very large investments. Before starting the operation, it is necessary to design the size and final shape of the pit in order to determine minable reserves and amount of waste to be removed. It is also needed to locate the waste dump, processing plant, access roads and to develop a production program. The ultimate pit limit depends upon many factors. One of the most important factors is the pit slopes which affect the stripping ratio and amounts of waste to be removed. When dealing with complex deposits in which the pit slopes may vary in different parts of the orebody due to slope stability requirements, it is necessary to take into account variable pit slopes in the designing of the pit limit. Determination of the pit limit 'in open pit mining is one of the ' most important design factors which may be considered many times during the life of the mine as the design parameters change in the future or more information is obtained during the operation. Therefore the use of a computer is essential in order to design the pit as rapidly as possible. As a result, a number of algorithms such as the various versions of the moving cone method, Lerchs-Grossmann algorithm, network or maximal flow techniques, Korobov algorithm, dynamic programming and parameterization techniques have been developed to determine the optimum ultimate pit limit since the advent and wide spread use of computers. The main objective of these algorithms is to determine the optimum pit limit in order to maximise the overall mining profit within the designed pit limit subject to the mining constraints. Of these, the Lerchs-Grossmann algorithm is well known for being the only method which always yields the true optimum pit limit. However, the algorithm which utilises graph theory was based on fixed slope angles that are governed by the block dimensions when it was introduced. In spite of the fact that many attempts have been made to incorporate variable slope angles, none of them provide an adequate solution where there are, variable slopes controlled by complex structures and geology. This algorithm is reconsidered and modified to deal with variable slope angles. It is assumed that the orebody and the surrounding waste are divided into regions or domain sectors within which the rock characteristics are the same and each region is specified by four principal slope angles including North, South, East and West face slope angles. Consequently slope angles can vary through the deposit to follow the rock characteristics and are independent of the block dimensions. In addition, two methods were also developed to estimate the four principal slope angles from geotechnical information to use as input parameters in the optimal pit, design algorithm. A general PC software was also developed to determine the optimum pit limit with variable slope angles for an open pit mine. The software is a Windows application that'can be implemented under 32-bit operating systems such as. Windows 95, Windows NT and. Windows 98. It is capable of taking advantage of all the computer memory and designing the optimum pit limit for complex, large and low grade deposits due to solving the memory limitation. The software includes both graphical and numerical presentation of the, input data and the results of optimisation. Two case studies have been used to validate the software developed

Spare-part management in a heterogeneous environment.

Spare-part management has a significant effect on the productivity of mining equipment. The required number of spare parts can be estimated using failure and repair data collected under the name of reliability data. In the mining industry, failure and repair times are decided by the operational environment, rock properties, and the technical and functional behavior of the system. These conditions are heterogeneous and may change significantly from time to time. Such heterogeneity can change equipment's reliability performance and, consequently, the required number of spare parts. Hence, it is necessary for effective spare-part planning to check the heterogeneity among the reliability data. After that, if needed, such heterogeneity should be modeled using an adequate statistical model. Heterogeneity can be categorized into observed and unobserved caused by risk factors. Most spare-part estimation studies ignore the effect of heterogeneity, which can lead to unrealistic estimations. In this study, we introduce the application of a frailty model for modeling the effect of observed and unobserved risk factors on the required number of spare parts for mining equipment. Studies indicate that ignoring the effect of unobservable risk factors can cause a significant bias in estimation

Spare Part Management Considering Risk Factors

The spare parts provision is a complex process, which needs a precise model to analyze all factors with their possible effects on the required number of spare parts. The required number of spare parts for an item can be calculated based on its reliability performance. Various factors can influence the reliability characteristics of an item, including operational environment, maintenance policy, operator skill, etc. Thus, the statistical approach of choice for reliability performance analysis should assess the effects of these factors. In this study, Reliability Regression Models (RRM) with risk factors have been used to estimate the required number of crane shovels in the Jajarm bauxite mine. For this, at the first stage, all risk factors and failure data have been collected. The required data were extracted from a database of 15 months, which were collected from different sources, such as daily reports, workshop reports, weather reports, meetings, and direct observations in the format of time to failures and risk factors. After that, the potential distribution has been nominated to model the reliability of the crane shovels bucket teeth. The Akaike information criterion and Bayesian information criterion have been used to identify the best fit distribution. The candidate distribution with the smallest AIC and BIC value is the best distribution that fits the data. After that, the required number of spare parts is calculated. The results show 18% differences between the forecasted number of required spare parts when considering and non-considering the risk factors

Monte Carlo reliability simulation of coal shearer machine

In this paper the Kamat-Riley (K-R) event-based Monte Carlo simulation method was used for reliability analysis of longwall shearer machine. Shearer machine consists of six subsystems; water, haulage, electrical, hydraulic, cutting arms and cable systems in a series network configuration. A shearer in the Tabas coal mine was selected as case study and its all failure data were collected and used for reliability analysis of subsystems. With negligible assumption of time to repair, a flowchart was built for programming the simulation process. The Matlab mathematical programming software was used for reliability simulation process. Finally the reliability plot of longwall shearer machine was achieved and upper and lower bound reliability were calculated. The results illustrate that the reliability of shearer machine reduces to zero in a period of 100h. There is a 50% chance that the shearer will not fail for the first 12h of operation.Validerad; 2013; 20130815 (behzad)</p

Tire demand planning based on reliability and operating environment

Tires represent a critical spare part in mines. There is a shortage of medium and large tires. In addition, with increased mining activities and the creation of new mines, the demand for tires has increased significantly. Thus, it is particularly important for mining engineers to identify tire characteristics and correctly manage the spare part inventory. Spare parts management is critical from an operational perspective, especially in asset intensive industries, such as mining, as well as in organizations owning and operating costly assets. A knowledge of the tires’ behavior (historical data) must be considered together with the operating environment conditions (covariates). This study uses multiple regression analysis based on Cox’s regression model to incorporate machine operating environment information into systems reliability analysis to estimate spare parts. It considers a proportional hazard model and a stratified Cox regression model for time independent and dependent covariates. Based on the results, the study develops a mathematical model for spare parts estimation at the component level for non-repairable parts (tires). It validates the outcomes using a case study of loader tires in Sungun mine in Iran. There is a significant difference in the results of spare parts forecasting and inventory management when considering and not considering covariates