Evaluation of Mud Weight Using Safe Mud Window Concept Based on Well Log Data: A Case Study of Well OP-002 in the North Sumatra Basin Area, Indonesia

In the drilling operation of well OP-002 which is located in the North Sumatra Basin at a depth interval of 2887 - 3186 m occurred partial loss, and caving at a depth interval of 500 - 1650 m, where the drilling problem is caused by the use of inappropriate mud weight. Safe mud window analysis is carried out by processing well log data to build PPFG (Pore Pressure Fracture Gradient) and 1D Geomechanics model using several calculation methods. Furthermore, the results of the calculation of pore pressure and fracture gradient are validated with well test data from the well OP-002, so the safe mud window can be determined, and can be used as a basis in the analysis of the drilling problems that occur. The optimum mud weight can minimize wellbore instability, with a limit value that must be greater than the collapse pressure, but not exceeding the minimum insitu stress limit. From the results of the mud safe window analysis, it can be concluded that at a depth interval of 500 - 1650 m caving occurs, because the density value used is smaller than the shear failure gradient, and at a depth interval of 1619 - 2829 m, the density value used is greater than Shmin. To overcome this problem, a mud wight with a safe mud window concept is recommended, namely the selection of the optimum mud weight to be used must be greater than the pore pressure and shear failure gradient and does not exceed the minimum horizontal stress and fracture gradient values.

DEA WINDOW ANALYSIS FOR INDONESIAN ISLAMIC BANKS’ EFFICIENCY

This study measures the efficiency of Islamic banks in Indonesia using Data Envelopment Analysis (DEA) window methods for 14 Indonesia’s Islamic banks covering the period spanning from 2011 to 2020.. The results show that the efficiency of Islamic banks averages 80% and exhibits increasing trend over the period 2011-2020. Based on stability measures, which are standard deviation (SD), Long Distance per Window (LDW), Long Distance per Period (LDP), and Long Distance per Year (LDY), we find the efficiency of PT Bank BRI Syariah (BSI) and PT Bank Syariah Mandiri (BSI) to be relatively stable.

Measuring the performance of the Indian banking industry: data envelopment window analysis approach

PurposeThis study aims to empirically evaluate the performance of commercial banks operating in India.Design/methodology/approachThe efficiency of the commercial banks is evaluated using the data envelopment analysis (DEA) approach. We measure the technical, pure technical and scale efficiency of the sampled conventional banks using the input-oriented model. We employed an extended DEA window analysis approach based on a panel sample of 47 banks in the Indian scenario. The period of study is from 2009 to 2018.FindingsThe results obtained from CRS and VRS measures envisage that Indian banks have failed to manage their inputs efficiently and convert them into outputs. It implies that Indian banks do not operate at an optimum level. Moreover, the results show that public banks exhibit superior efficiency scores followed by private and foreign banks. Apart from the aggregate sector level, we also investigate the performance of Indian banks at the individual level for in-depth analysis. The individual bank-level analysis reports that the public sector banks (PSBs) are the most efficient followed by foreign banks, whereas, the least efficient are the private banks.Research limitations/implicationsThe findings of our study have implications for government, financial institutions and policymakers to access the verve and flexibility of the Indian banking system. The government should consider restructuring inefficient banks to enhance overall performance. This can be considered by improvement in managerial efficiency, efficient allocation of scarce resources and appropriate scale of operation. However, the findings of the study should be interpreted in light of the period of study for the banks being operational (as we filter out banks that ceased to exist) in India and empirical methods employed. The results may vary if alternative measures are used.Originality/valueThe present paper investigates the efficiency of the Indian banking sector employing the Data Envelopment Window Analysis (DEWA) technique. To the best of our knowledge, the present study is perhaps the first one to employ the DEWA measure on the Indian banking industry to gauge their performance over time.

DEA Window Analysis Efficiency Measurement of Selected Nigerian Bank

This study analyzes the performance efficiency of six selected banks in Nigeria for the period 2010 – 2016. DEA window analysis was employed to establish the performance efficiency of the selected banks. The analysis is based on panel data for the period under review. The result of the DEA window analysis for the reviewed period showed that the average efficiency scores under constant returns to scale ranged from 84% to 91%. Under the variable returns to scale, the average efficiency scores ranged from 91% to 95%. The average inefficiency of the selected Nigeria commercial banks under the constant returns to scale model was in the range 9 – 16%. This inefficiency could be attributed to the excess of customers deposits on the balance sheet of the selected banks. The average scale efficiency for the banks was 93%. Guaranty Trust Bank was the most efficient bank on all measures. United Bank for Africa was the most inefficient bank under constant returns to scale and variable returns to scale. It was however, more scale efficient than three other banks, an indication that its inefficiency cannot be attributed to inappropriate scale size.

A Decision Support Model for Measuring Technological Progress and Productivity Growth: The Case of Commercial Banks in Vietnam

The interactive relationship between the banking system and enterprise makes up the role that affects a national economy. Significantly, the relationship between banking and technology has become tighter over the past few decades. An assessment of bank performance is critical for understanding their position and provides valuable information to practitioners. In this paper, we assess the performance of the top 18 commercial banks in Vietnam during 2015–2019. The assessment utilizes two data envelopment analysis (DEA) models while involving the banks’ performance in six dimensions, including assets, deposits, operating expenses, liabilities as inputs, while treating loans and net income as outputs. Using the Malmquist measurement, the total productivity growth indexes of the banks are obtained, which are decomposed into technical and technological evolutions. Window analysis is used to compute the efficiencies of the banks in every single year in 2015–2019. From the results of Malmquist, most banks are found to decrease their Malmquist productivity indexes from 2015 to 2019, wherein both of their technical and technological indexes declined. Window analysis indicates B6-SHBank, B1-Vietinbank, and B18-PetrolimexGroup as the most efficient banks during 2015–2019, and in the interim, B16-BaoVietBank, B11-NationalCitizen, and B13-VietnamMaritime ranked on the bottom line. The managerial implications of this research help to reflect the comprehensive insights of the top Vietnamese commercial bank performance and offer a strategic guideline for decision-makers toward sustainable development in the banking industry.

ÁP DỤNG PHƯƠNG PHÁP FSA ĐỂ PHÂN TÍCH PHÓNG XẠ MÔI TRƯỜNG CHO MẪU HÌNH HỌC MARINELLI

Trong bài báo này, chúng tôi áp dụng phương pháp FSA (Full Spectrum Analysis) vào phân tích xác định hoạt độ các đồng vị phóng xạ tự nhiên 226Ra, 232Th, 40K trong các mẫu đất hình học Marinelli bằng hệ phổ kế gamma phòng thí nghiệm. Mẫu chuẩn IAEA-SL-2, KL-01 được sử dụng làm mẫu phân tích để kiểm tra độ tin cậy của phương pháp FSA. Kết quả phân tích các mẫu đất bằng phương pháp FSA được so sánh với EWA (Energy Window Analysis). Từ đó, chúng tôi đánh giá những ưu điểm của phương pháp FSA so với phương pháp phân tích truyền thống EWA.

Long-Term Economic Sustainability of Humanitarian Logistics—A Multi-Level and Time-Series Data Envelopment Analysis

Although resources are scarce and outputs incorporate the potential to save human lives, efficiency measurement endeavors with data envelopment analysis (DEA) methods are not yet commonplace in the research and practice of non-government organizations (NGO) and states involved in humanitarian logistics. We present a boot-strapped DEA window analysis and Malmquist index application as a methodological state of the art for a multi-input and multi-output efficiency analysis and discuss specific adaptions to typical core challenges in humanitarian logistics. A characteristic feature of humanitarian operations is the fact that a multitude of organizations are involved on at least two levels, national and supra-national, as well as in two sectors, private NGO and government agencies. This is modeled and implemented in an international empirical analysis: First, a comprehensive dataset from the 34 least developed countries in Africa from 2002 to 2015 is applied for the first time in such a DEA Malmquist index efficiency analysis setting regarding the national state actor level. Second, an analysis of different sections in a Rohingya refugee camp in Bangladesh is analyzed based on a bootstrapped DEA with window analysis application for 2017, 2018, and 2019 quarter data regarding the private NGO level of operations in humanitarian logistics.