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One of the major challenges confronting the oil and gas industries worldwide is lost circulation, which results in significant amounts of drilling non-productive time. Drilling fluid loss may result in additional drilling issues such as stuck pipe, borehole instability, or a potential well control event. Al-Omar field in Syria is one of the world's largest oil fields. When drilling through the Shiranish formation, wells in this field are highly susceptible to lost circulation problems. The lost circulation processes in Al-Omar Oil field are close to complete failure due to leakage. This issue is important in field development operations and is given great attention. If the process is carried out according to the correct course, there is expected to be a significant decrease in Non-Productive Time (NPT) resulting from the loss. On the other hand, this paper will consider the cost of remedy processes and the NPT relating to the remedy method used. The loss in the Shiranish layer comes from more than 700 oil wells drilled in Syria and is discovered using field sources and various engineering reports. Remedy processes were divided according to the field data into three losses: (1) partial loss, (2) severe losses, and (3) complete losses. In this paper, we use economic forecasts that are considered significant from an economic point of view when setting remedy processes. In addition, we employ the incoming probabilities method and remedy the costs of lost circulation in Al-Omar field in Syria. We perform hundreds of remedies on the three types of losses, calculate the Expected Monetary Value (EMVs) in all operations to reduce NPT and high costs, and choose the least costly method to process EMV that we can apply correctly and practically and is acceptable to remedy all types of losses. And if the losses continue after using the proposed solutions, we can suggest some changes in the design of the well to prevent losses. The methods used in dealing with the problem of lost circulation are some of the most significant challenges we face at Al-Omar oil field in Syria, and as a result, the data analysis process provides a clear path for the Syrian fields. It can also be used in other similar formations in the Middle East. It also applies to configurations with properties identical to Al-Omar oil field.

Despite its challenges, the building sector remains a crucial pillar of Iraq's economic progress. This study aims to develop and evaluate machine learning models for predicting the productivity of housing projects in Iraq based on project design and implementation characteristics. Three advanced prediction techniques were used, including artificial neural networks (ANN), extreme gradient boosting (XGBoost), and support vector regression (SVR). Actual data from 67 housing projects were used, including seven input variables and one output variable represented by implementation productivity (real time). The evaluation results showed that the XGBoost model achieved the highest predictive accuracy with a determination coefficient of R 2  = 0.998 and a relative error (MAPE) of 0.8%. In comparison, the ANN model also performed well (R 2  = 0.92 and MAPE = 4.1%). In contrast, the SVR model performed less well with an accuracy of R 2  = 0.813 and MAPE = 6.6%. The results of the feature importance analysis also revealed that the most influential factors in productivity were built-up area, followed by the quantities of brick and concrete works. It can be implied that machine learning techniques, particularly XGBoost and ANN, provide effective and accurate tools to support decision-making in the early stages of housing project planning by predicting executive performance based on project data.

The housing sector in Iraq is facing growing demand. However, productivity challenges continue to hinder the timely and efficient delivery of residential projects. Recognising the urgent need to increase construction productivity (CP) in this sector, this study investigates critical factors that impact the performance of horizontal housing projects in Iraq. The novelty of this research lies in its focus on housing skeleton tasks, such as foundations, brickwork, and slabs, which are pivotal to project efficiency. A structured questionnaire was developed, encompassing 47 factors distributed across the planning, design, and construction phases. The survey was administered to a range of industry stakeholders, including project managers, consultants, engineers, and supervisors, and it achieved a high response rate of 79.6%. The results were analysed via a modified weighted relative importance index (RII), which incorporates respondents’ experience levels to yield a nuanced understanding of each factor’s impact. The key findings reveal that planning (RII = 0.874), team size (RII = 0.856), quantity of brickwork (RII = 0.838), supervision (RII = 0.815), and unit area (RII = 0.793) are among the most influential factors on CP. These insights provide valuable guidance for researchers and practitioners in identifying areas for strategic improvement, ultimately supporting the capacity of Iraq’s housing sector to meet escalating demands.

BackgroundChildren living in orphanages face an increased susceptibility to malnutrition due to inadequate nutrition and psychological factors, in comparison to children who stay with their parents. A considerable proportion of institutionalised children remain unreported, and there is a dearth of information regarding the nutritional status of these children in Pakistan. This study set out to evaluate the status of malnutrition in the orphanages of Social Welfare Department Punjab.MethodologyA multicentre analytical cross-sectional study was conducted from 12 December 2021 to 30 June 2022, with 255 study participants (aged 6–18 years) in seven orphanages (4 girls, 3 boys) out of 12 orphanages of government of Punjab. Anthropometric measurements were taken using standardised measuring instruments, and data were collected using structured questionnaire. Subjects were classed as malnourished based on body mass index for age and height for age reference growth charts of WHO 2007. Binary logistic regression was used to identify potential risk factors of malnutrition in orphanages.ResultsThe study identified 36.1% malnourished children, of whom, 65.2% were orphans and 34% destitute children. The study reported 9.4% moderately underweight, 2.4% severely underweight, 4.3% overweight and 1.2% obese children and adolescents. The study established 17.6% moderately stunted, and 7.1% severely stunted children. Age at admission to orphanages (9–13 years) and lack of milk and meat consumption were identified as significant predictors of malnutrition in institutionalised children of Punjab.ConclusionCentral Punjab had the highest malnutrition rate compared to North and South Punjab. Micronutrient deficiencies were more pronounced in South Punjab orphanages. Effective prevention of malnutrition requires early assessment of malnutritional risk in Pakistani orphanages.