This article aims to investigate how poor people in developing countries gain benefit from globalization by examining the opportunity to work as migrant worker. To respond the research gap, this study adopts a case study approach with a fieldwork survey in East Java Indonesia. The result indicates that diaspora strategy is almost impossible for the poor if he or she notices a high possibility for the employer to cheat those who work in domestic jobs, like nanny, driver, or baby sitter. Unless protection from both house and home countries is available, the diaspora strategy is not a better option for the migrant-work applicant. The paper contributes to the scholarly interest in the diaspora strategy to interrogate the assumption underlying the Migration-as-Development (MAD) discourse.On the supply side, the labor market environment in destination countries is main determinant of migration, such as labor institutions bring impact on expected employment and wages in the labor market, thus influencing incentives for migrants to move in a particular country [8]. The need to promote diaspora strategy springs from the economic disparities between the home country and the envisaged 1st Social and Humaniora Research Symposium (SoRes 2018)
Introducing rotary steerable systems (RSS) to the drilling industry has extended the directional drilling envelope to new horizons. Its role in better hole cleaning, faster rate of penetration (ROP), and less stuck pipe incidents is unquestionable. However, it comes with more economical challenges in terms of operating rates and Lost-in-Hole (LIH) charges. Many factors control the operator decision to run RSS rather than positive displacement mud motors (PDMs) — which have been the industry standards for decades — and vice versa. The intent of this paper is to introduce an advisor system based on machine learning that makes the selection process easier and more straightforward. The system predicts total section time (including casing running) and cost for both RSS and PDM, the user may use either section time or cost criterion to assess which technology is preferred based on drilling campaign needs. The system input features include offset data of interval measured length, formations encountered, ROP, dogleg severities, operating cost of both technologies, rig operating cost, and many other variables. Input data to the system are divided into training and testing data for more reliable modeling. Many algorithms are tested to avoid over-fitting or under-fitting of training data. The past performance of both RSS and PDM technologies in different formation plays a major role in determining the result. The result comes in both visual and tabulated forms to give the drilling engineer both quick impression and detailed insights about the selection process. As the result depends on formation-specific performance, it would change between wells depending on the formations thickness at these wells. This means that the result is not fixed per field and may vary considerably based on small changes of input. The use of this advisor system should help in speeding up operator companies’ decisions and improving drilling performance and cost-effectiveness. This occurs through automating the whole process and making best use of offset drilling data thought implementation of machine learning algorithms.
The drilling cost represents a significant portion of total well cost, especially for directional wells. The major component of drilling cost is determined by how fast a well is drilled, i.e., rate of penetration (ROP). Many factors affect ROP including bit type and condition, formation characteristics, and drilling parameters. One factor that is usually ignored is the direction of drilling (well azimuth). Literature introduces many thoughts about effect of well azimuth on important issues like wellbore stability, but unfortunately it lacks high quality researches about azimuth/performance relationship. The objective of this paper is to test the influence of drilling azimuth on drilling performance. This relationship was studied in terms of ROP, and drillability exponent (DEXP), which can be used for drilling performance assessment besides well control applications. Drilling data from different areas was processed and categorized by fields and formations. In-Depth statistical analysis was performed, and visualization tools like heat maps were generated to assess the relationship between drilling performance and drilling azimuth. The noise effect of various factors like bit type and condition was normalized so that the resultant output was expressive of azimuth effect. The resultant heat maps act in the same manner as mechanical earth models (MEM) were used for wellbore stability studies. This occurs by suggesting the most appropriate azimuth to drill the well more efficiently in terms of time and power consumption. Python libraries were implemented for the sake of fast data processing with visually comprehensible output reports that were used for the planning of future drilling campaigns. These reports have ROP and DEXP graphed against well azimuth and inclination to magnify the effect of both horizontal earth stresses and attack angle of the bit. Although the output is conclusive in most cases, this is not the fact in all wells and further investigations are required. The result of this research is very beneficial tool that can be used while planning directional trajectory of oil and gas wells to speed up the drilling operations and cutting the cost back. By allocating suitable targets to certain surface locations, the required horsepower to drill the well at the same ROP is reduced and the rig time at the same power is significantly cut.
Planning high-angle wells involves diverse areas; one of the most important of these areas is torque and drag (T&D) management. Not only could uncontrolled T&D cause various drilling problems like drill string (D/S) failures, casing wear, stuck pipes, and slow rates of penetration but it could also entirely stop the drilling progress, if torque and/or drag exceed rig or string capabilities. Modeling T&D in advance would alleviate these problems by prediction of friction forces to be encountered and urging the drilling team to take the required measures to mitigate these forces or upgrade the drilling hardware (rig equipment and/or D/S). Modeling T&D is still a complex and time-consuming job to be carried out at the rig site while drilling, so that an accurate and rig-friendly model would be very useful to industry. In this work, a novel and simple model had been developed to predict T&D values while drilling both curve and tangent sections of high-angle wells based on a soft-string concept, in which the D/S is assumed to be a chain lying on the lower side of the well that can transmit torsional forces. Despite the simplicity of the calculations, the model accounts for components of drilling torque that are overlooked in most complex packages. Friction within the top drive system had been considered to predict the torque acting on the D/S only. In addition, the torque applied on the D/S by the viscous drilling fluid was accounted for by reversing the concept of viscometers. The model proved to be practical and reliable for the two-dimensional wellbore and thus is superior in terms of quick field application. The developed model was tested using data from the Western Desert, Egypt. Statistical analysis had been used to assure the accuracy of the proposed model and to assess the effect of different drilling parameters and practices on both T&D. The reliability of the model had been proven with a negligible error for drag calculations and 10% error on average for torque calculations. Also, the effect of distance between successive survey stations on T&D modeling had been proven mathematically. This research narrows the gap between theory and practice by studying the dominant factors and determining the extent of the effect of each of them on wellbore friction forces. In addition, the work sheds light on the best practices concluded from the application of the developed model on field data.
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