Research Subject:Drilling operations frequently encounter numerous challenges that can lead to significant financial, human, and environmental losses. Therefore, predicting potential problems before they occur and implementing necessary preventive measures is crucial to minimizing risks. In this context, this study investigates the impact of employing artificial intelligence (AI) algorithms to forecast drilling complications using real-time mud logging data collected from existing wells in an Iranian oilfield. Research approach: A hybrid architecture combining Long Short-Term Memory (LSTM) and Fully Connected neural networks was developed for the identification and detection of anomalies such as kicks and stuck pipe. Given the scarcity of these anomalies in the dataset, which could adversely affect model accuracy and performance, the Synthetic Minority Oversampling Technique (SMOTE) was applied to balance class distribution and enhance the overall effectiveness of the network. Furthermore, the influence of varying hyperparameters on reducing network error was systematically analyzed. Main Results: Various network architectures and structures were examined. The experimental results indicated that the optimal model achieved an accuracy of 94.45% on the testing dataset with the following hyperparameters: a lookback of 7, a learning rate of 0.001, a dropout rate of 0.2, a batch size of 32, and a four-layer network architecture with 512, 256, and 256 units in the first, second, and third hidden layers, respectively. This configuration yielded higher accuracy and fewer false alarms in anomaly detection compared to other tested models. Based on the obtained results, this approach demonstrates significant potential for real-time anomaly detection in drilling operations.

Pore pressure and Fracture gradients are the key inputs for geomechanical applications. Knowledge of these pressures is essential for cost-effective drilling, safe well planning and efficient reservoir modeling. The main objective of this study is to determine the safe mud weight windowusing well log data at one of the gas fields in south of Iran. To obtain this goal, the formation pore pressure and fracture gradient are estimated. The formation pore pressure is estimated from well logging data by applying Eaton’s prediction method with some modifications. In order to determine the fracture pressure, the estimated pore pressure is used along with calculating Poisson’s ratio from compressional and shears velocities. The estimated fracture and pore pressures are used as the top and bottom limits of the safe mud weight window. Finally, in order to generate the 3D model, well-log-based estimated fracture and pore pressures are upscaled and distributed along the 3D structural grid with a geostatistical approach in Petrel software. The obtained model is validated using a mud weight data which was not used in the modeling. The result shows that the estimated model is appropriate for the purpose of drilling plan.

The Kangan and Dalan formations have long been considered as gas reservoirs due to their deep burial history. However, this study investigated the type of hydrocarbon fluid in these reservoirs based on the geochemical interpretation of mud gas logging and petrophysical evaluation in the Persian Gulf. The study was conducted in three stages. Firstly, well logs were evaluated under the assumption that the reservoir is an oil-type reservoir. In the next step, the type of fluid was identified based on the geochemical interpretation of mud gas logging, using the Haworth and Pixler methods. The final fluid type was determined based on the integration of geochemical methods. In the last step, a petrophysical re-evaluation was done in the susceptible oil zone. According to the primary petrophysical interpretation, there are three oil zones including K1, K2 and K4 in the reservoir. Meanwhile, based on the geochemical interpretation of mud gas logging, K1 and K4 zones are gas type and only the K2 zone contains oil-type hydrocarbon. Petrophysical re-evaluation of the K2 zone (identified as oil-type reservoir zone) indicated an effective porosity of 6% and water saturation of 29%. The results of this study suggest that paying attention to mud gas logging interpretation when determining the appropriate interval for perforation and selecting the appropriate zones for reservoir tests can reduce drilling costs and time.

Sustainable forest management in Caspian forests of Iran requires cost-benefit analysis in order to maintain the quality of timber stands during logging operations. This study was done with the aim of cost and gross revenue computations from current logging (net present value) in three adjacent compartments in district 2 of Shafroud watershed. Motion and time studies were carried out during felling, bucking, skidding, loading, and hauling operations. The weighted average price for one cubic meter wood was computed 2565090 Rials in three compartments. The study of costs per cubic meter showed that 128314 Rials were devoted to logging costs, 158175 Rials to road construction costs, 8161 Rials to skid trail construction costs, and 71223 Rials to overhead costs. On the other hand, the total production cost per m3 is 365873 Rials of which 90% pertains to fixed costs. The results of this study suggest that government should increase the stumpage price, and governmental logging companies should pay costs due to severe damages to forests after logging operations.

Introduction: In recent years, one of the most important challenges in the management of Hyrcanian forests is the continuation of the process of forests logging or its logging ban (forest respiration). Therefore, the purpose of this study is to identify the strengths, weaknesses, opportunities and threats of logging plans and forest logging ban from an economic and social perspective and to provide appropriate management strategies in this field and prioritize them as a tool to reduce these challenges. Material and methods: The study was conducted in Mazandaran Province (Sari City) using field observations and questionnaire completion. The statistical sample of the study consisted of experts and faculty members specialized in the forestry field and rural communities on the margins of forests. In this study, entropy and SWOT techniques were used for data analysis. Results and discussion: The results of the study identified 4 strengths, 6 weaknesses, 8 opportunities and 2 threats for forest logging and 2 strengths, 9 weaknesses, 4 opportunities and 8 threats for forest logging ban. According to the SWOT matrix results, the most important strengths and opportunities for forest logging included reducing wood smuggling and meeting the country's wood needs, and the most important weakness and threats included logging costs and nature lovers' dissatisfaction, respectively. Also, the most important strengths and opportunities of the forest logging ban include increasing the economic value of forest environmental services and the tendency towards wood cultivation, and its most important weaknesses and threats were the reduction of employment and the increase of wood smuggling, respectively. Conclusion: According to the results of data related forest logging and forest logging ban (forest respiration) of the forests of the study area and based on analysis of QSPM matrix results, most appropriate management strategy for forest logging and forest logging ban was an offensive strategy and a defensive strategy, respectively.

CMC polymer is used as an additive to decrease water loss and mud-cake-thickness in mud drilling. In this study, the effect of CMC and CMC nanoparticles on water loss and mud-cake-thickness in mud drilling is investigated. CMC nanoparticles are made by using of ball milling and their size is measured by Particle size analyzer. CMC and CMC nanoparticles which were prepared by Hamilton batch mixer and with certain percent suggested by API, were added to the water-based mud drilling. The amount of water loss and mud cake thickness was measured by filter press. It was found that adding CMC nanoparticles in comparison with conventional CMC resulted in desirable reduction of amount of water loss and mud cake thickness.