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Before adjustable parameters of drilling can be optimized, it is necessary to have a high-accuracy model for predicting the rate of penetration (ROP), which can represent the effects of drilling parameters and formation-related factors on the ROP. Accordingly, the present research attempts to use different algorithms, including convolutional neural network (CNN), simple form of least square support vector machine (LSSVM) and its hybrid forms with either particle swarm optimization (PSO), cuckoo optimization algorithm (COA), or genetic algorithm (GA), and also hybrids of multilayer extreme learning machine with either of COA, PSO, or GA, to model ROP based on mud-logging and petrophysical data along two wells (Wells A and B). For this purpose, firstly, median filtering was applied to the data for the sake of denoising. Next, petrophysical logs were upscaled to make their scales matched to that of mud-logging data. Then, the nondominated sorting genetic algorithm (NSGA-II) was combined with a multilayer perceptron (MLP) neural network to select the set of the most significant features for estimating the ROP on the data from Well A. Feature selection results showed that the accuracy of the estimator models increases with the number of parameters to a maximum of seven, beyond which only subtle enhancements were seen in the modeling accuracy. Accordingly, the ROP was modeled using depth, bit rotary speed, mud weight, weight on bit, compressive wave slowness, total flow rate, and neutron porosity. Training the hybrid, CNN, and LSSVM algorithms using the training data from Well A showed that the model built upon the CNN algorithm tends to produce the smallest root-mean-square error (RMSE = 1.7746 ft/hr), as compared to the other models. In addition, the smaller difference in error between the training and testing phases (RMSE = 2.5356 ft/hr) for this model indicates its high generalizability. This fact was proved by the lower estimation error of this model for predicting the ROP at Well B, as compared to other models. [ABSTRACT FROM AUTHOR]

The aim of this study is the integration of mud logging and wire-line logging data to detect overpressure zones in Kareem Formation (Middle Miocene), Ashrafi Field, Gulf of Suez, Egypt. The study is performed for the three wells Ash_H_1X_ST2, AshI1X_ST, and Ash_K_1X. The prediction of the abnormal pressure is a quite important factor in the design of the well, where it contributes to avoid many problems during the drilling process and maintain the formation fluids. The abnormal pressure zones occur due to major changes in lithology, petrophysical properties, and fluid type, where these factors lead to differences in pore pressure from hydrostatic pressure, and their prediction is achieved by utilizing rock cuttings, D-exponent, and methane gas; in addition, the porosity and water saturation are estimated from mud logging as real-time data and compared to wire-line logging (resistivity, porosity, and permeability) to determine these zones. The concept of detecting abnormal pressure zones in this study is based on defining the marked changes in the D-exponent trends that arise from the variations of the fluids and the lithology of the Kareem Formation. Therefore, these trends are integrated with the petrophysical parameters such as resistivity, porosity, and permeability from wire-line logging to detect the overpressure zones. So, the overpressure zones are detected in the intercalated sand and shale intervals of the studied wells within the Kareem Formation and are mostly marked by a decrease in the reservoir quality such as permeability, as well as an increase in the resistivity and D-exponent. The thickness of the overpressure zone in Ash_H_1X_ST2 well is influenced by the marl content that reaches up to 80%. The integration results are summarized to determine the average depths of the overpressure zones for the Kareem Formation in the three studied wells. The zone average depths in the Ash_H_1X_ST2 well range from 6022.50 to 6093.30 ft, whereas the zone top is detected in the AshI1X_ST well at the top of the Kareem Formation (6580.00 ft), and the zone bottom at average depth of approximately 6704.20 ft, in addition, the zone average depths in the Ash_K_1X well range from 7718.33 (top) to 7833.33 ft (bottom). [ABSTRACT FROM AUTHOR]

Wireless mud-logging systems have been employed in petroleum exploration to guarantee efficiency and safety in well-drilling. For the wireless networks in well-drillings, the tradeoff between throughput and fairness has great relation to the type of wells. The throughput is of the most importance to deep wells, while fairness is of primary concern for shallow wells. In this study, this challenge is addressed by using 2-SIC (successive interference cancellation) techniques. The throughput maximization and the fairness maximization problems are further formulated based on an imperfect 2-SIC model, which differentiates our work from existing ones. In view of the hardness of these problems, a low-complexity approximation algorithm for maximizing throughput and a low-complexity heuristic algorithm for maximizing fairness are proposed. To evaluate the proposed schemes, extensive simulations are performed. According to simulation results, the throughput performance could be improved by 23% using our approximation algorithm of throughput maximization, while the Jain's fairness index could reach 0.975 using our algorithm of fairness optimization. [ABSTRACT FROM AUTHOR]

Background: Integrated Ocean Drilling Program Expedition 338 was the second scientific expedition with D/V Chikyu during which riser drilling was conducted as part of the Nankai Trough Seismogenic Zone Experiment. Riser drilling enabled sampling and real-time monitoring of drilling mud gas with an onboard scientific drilling mud gas monitoring system ("SciGas"). A second, independent system was provided by Geoservices, a commercial mud logging service. Both systems allowed the determination of (non-) hydrocarbon gas, while the SciGas system also monitored the methane carbon isotope ratio (δ13CCH4). The hydrocarbon gas composition was predominated by methane (> 1%), while ethane and propane were up to two orders of magnitude lower. δ13CCH4 values suggested an onset of thermogenic gas not earlier than 1600 meter below seafloor. This study aims on evaluating the onboard data and subsequent geological interpretations by conducting shorebased analyses of drilling mud gas samples. Results: During shipboard monitoring of drilling mud gas the SciGas and Geoservices systems recorded up to 8.64% and 16.4% methane, respectively. Ethane and propane concentrations reached up to 0.03 and 0.013%, respectively, in the SciGas system, but 0.09% and 0.23% in the Geoservices data. Shorebased analyses of discrete samples by gas chromatography showed a gas composition with ~0.01 to 1.04% methane, 2 - 18 ppmv ethane, and 2 - 4 ppmv propane. Quadruple mass spectrometry yielded similar results for methane (0.04 to 4.98%). With δD values between -171%o and -164%o, the stable hydrogen isotopic composition of methane showed little downhole variability. Conclusions: Although the two independent mud gas monitoring systems and shorebased analysis of discrete gas sample yielded different absolute concentrations they all agree well with respect to downhole variations of hydrocarbon gases. The data point to predominantly biogenic methane sources but suggest some contribution from thermogenic sources at depth, probably due to mixing. In situ thermogenic gas production at depths shallower 2000 mbsf is unlikely based on in situ temperature estimations between 81°C and 85°C and a cumulative timetemperature index of 0.23. In conclusion, the onboard SciGas data acquisition helps to provide a preliminary, qualitative evaluation of the gas composition, the in situ temperature and the possibility of gas migration. [ABSTRACT FROM AUTHOR]

This paper presents the results of a case study performed in Well BT-1 in the Blacktip field; an offshore field located in northern Australia. An analytical model based on mud logging data was used to estimate a formation's strength in Well BT-1. The drilled well is vertical and the input data to the model included the rate of penetration, rotary speed, weight on bit and torque values of the bit obtained from mud logging data. The final model was completed in several steps: estimation of the bit constants from either lab or field data, backward calculation of rock strength using a drilling performance model, smoothing of the estimated log to obtain the apparent rock strength log and classifying the formations into zones with similar strength values. The results of a log-based approach carried out in Well BT-1 were used to do the modelling. The results presented here indicate that the drilling performance model is able to predict the formation strength reasonably well. Based on the results, care should be taken when the model is used for the intervals where the bit is worn and dull, since within these intervals the model tends to over-estimate the rock strength. [ABSTRACT FROM AUTHOR]

As a result of drilling mud filtrate invasion of a formation saturated with oil, gas and natural water, the distribution of the immiscible phases and the electrophysical characteristics of the near-well zone change as compared with its initial state. Taking this change into account is necessary for successful interpretation of electrical well logging data. In this paper, on the basis of the equations of immiscible fluid flow through a porous medium and the system of transfer equations with account for instantaneous salt exchange between the filtrate and the natural water inside the pores, the regions of initial formation fluid saturations for which in the invasion zone the displacement fronts retain their relative position are determined. [ABSTRACT FROM AUTHOR]

The article focuses on optimization problem in design of centrifuges for cleaning of drilling muds. The centrifuges remove an appreciably greater percentage of drilled rock, which particles contaminate the drilling mud, than hydrocyclone plants, and with considerably smaller losses of expensive liquid phase. Centrifuge efficiency during the removal of drilled rock from the mud is determined by the volume of solid phase extracted by the centrifuge from the mud per unit time. The greater the discharge of solid phase, the more efficient the centrifuge.