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Summary Steering drilling is used for exploring oil, natural gas, and other liquid and gaseous minerals. Steering drilling consists of high-efficiency drill bits, steering power drilling tools, and logging while drilling (LWD) and is used in petroleum drilling engineering. This paper mainly discusses subhorizontal drain geosteering, one of the methods of guided subhorizontal drilling. We use the currently popular deep learning method to conduct intelligent guided drilling. Geosteering is a sequential drilling decision process under uncertain stratum environment. However, the current geosteering drilling process relies heavily on manual work and has no use of temporal context. This paper aims to solve decision-making of geosteering in deep well (between 4500 and 6000 km) or ultradeep well (between 6000 and 9000 km). To this end, we make three contributions: (1) a wide-angle eye mechanism to obtain more geological information; (2) an asymmetric peephole convolutional long short-term memory (APC-LSTM) approach for geosteering drilling decision, whose input data were assembled with the wide-angle eye mechanism; and (3) use of the deep convolution generative adversarial networks (DCGAN) model to generate simulated logging data and conduct experiments in the simulation environment to verify our proposed method. APC-LSTM can capture the spatial-temporal correlation better between different strata for decision-making. Meanwhile, the APC-LSTM drilling decision model achieved better performance than other advanced methods in two drilling data sets. Tested in a simulative drilling environment, our proposed model achieves excellent application effect. Moreover, our method has been applied to the wells of oil field in practice.

The impact of moisture on gas adsorption capacity reduction on coal has been well recognized, and empirical correlations are widely used to quantitatively evaluate the moisture effect. However, few studies are found on fundamental modeling of the moisture effect on gas adsorption capacity. In this work, two theoretical models on the basis of the extended Langmuir theory (EL-based) and the ideal adsorbed solution theory (IAS-based) were developed to account for the gas adsorption capacity with different pressures and moisture contents. With the parameters determined from the gas adsorption on dry samples and water adsorption on samples under atmospheric conditions, both models are able to predict the gas adsorption capacity under combined effects of gas pressure and moisture content. The models were verified through a set of experimental data from a coal sample from Australia, and they were further applied to describe the methane adsorption behavior on a coal sample from New Zealand. The results demonstrat...

Formation and lithology identification based on well logging curves reflecting geophysical response characteristics is fundamental for drilling planning and reservoir recovery. For the purpose of providing efficient, accurate, and comprehensive insights for drilling operation decisions, the present research evaluates three typical supervised learning algorithms based on machine learning, e.g. Adaboost, decision tree and support vector machine (SVM). By comparing the prediction results from three typical classification algorithms based on performance metrics such as accuracy, precision, recall, F1 score, Adaboost and decision tree are found to present more accurate prediction with relatively higher accuracy, precision, recall and F1 score. The prediction accuracy is positively related to the training data set proportion for all three approaches. Decision tree approach spends less computation time while still provides favorable prediction scores. The accuracy of prediction gradually increases as the number of logging features increases. Accuracy for most parameter combinations beyond four logging parameters can be up to 90%. The neutron porosity and the spontaneous potential are considered as the most influential parameters affecting the prediction accuracy.

Channel fracturing revolutionarily changes continuous sanding into discontinuous distribution by wrapping proppant particles with a degradable fiber and then injecting these packs or pillars into the formation. Channel fracturing creates open channels between pillars and substantially increases the fracture conductivity. The pulse time of injecting proppants can influence pillar spacing, thereby changing the opening of the fracture. Under different formation conditions, determining a proper pulse time to improve the wellbore productivity is the key problem in channel fracturing. On the basis of Hertz contact theory, we assume that a proppant pillar is a standard cylindrical indenter. We also calculate the penetration depth beneath the original surface. In this manner, we obtain the residual fracture width under a certain confining stress. This residual fracture width serves as our optimization objective. Then, by combining the flow conservation equation, we find that a restriction relationship exists between the pillar spacing and pulse time. By controlling the variables, we adjust the parameters to find the maximum fracture width. The fracture width reaches its maximum when the horizontal spacing equals vertical spacing. A set of simulations are completed and analyzed. The results show that the pulse time, pillar spacing, and formation parameters such as the closure pressure, elasticity modulus and initial crack width, can significantly affect the supporting effectiveness of the proppant pillar. Critical points exist when these parameters increase or decrease to a certain level. For the convenience of field operation, a reference plate is provided. A proper pulse time can be determined on the plate under certain formation conditions, thereby providing theoretical guidance for actual channel fracturing.

A large amount of experimental data for methane adsorption capacity in shale are available and the Langmuir model is capable to describe most of these adsorption isotherms. Two parameters used in the Langmuir model are the Langmuir pressure constant P L and the Langmuir volume constant V L . However, experimental data also demonstrate that the gas adsorption capacity of shale is greatly affected by temperature and the classic Langmuir model, with temperature independent V L and temperature dependent P L , could not well interpret the experimental data. This is partly attributed to the isosteric heat of adsorption, which makes V L vary with temperature. The change of V L with temperature for gas adsorption in shale and its dependency on shale properties are still not well understood. In this study, the variation characteristics of V L with temperature for gas shale are investigated through a modeling analysis on the published gas sorption data on the shales from the US, China, Canada, and etc. The classic Langmuir model is improved by considering the temperature dependent V L . The results show that the improved Langmuir model can reasonably describe the shale gas sorption data with less fitting parameters required and it allows a better understanding of gas sorption capacity under the combined effect of temperature and pressure. In addition, the model parameter analysis indicates that the adsorption capacity of shale samples with lower TOC is more likely to be affected by temperature change on the basis of the limited data available. More experiments are required to investigate how the shale properties affect the thermal impact on the gas adsorption capacity on gas shale.

As pore size of gas shale ranges widely, various flow types including viscous flow, slip flow and Knudsen flow could coexist during shale gas production. Currently, different models have to be applied to describe the gas transport behaviours in different flow regions and the selection of the models is highly dependent on the flow classification criteria. This also brings difficulty in selecting proper gas flow models near the flow region boundaries, which again depends on the flow classification criteria. To address this problem, this study proposes a unified method to describe the gas flow in the whole spectrum of flow regions by transforming all the gas transport equations for different flow types into a general form. The modelling results by the unified method are comparable with that by the previous models, but less input parameters are required for the unified method. Since the unified method provides a continuous permeability kea for all flow regions, it avoids discontinuity at the boundary of different flow regions caused by using piecewise flow models. The method is able to depict the gas permeability evolution with reservoir pressure change and is applied to interpret the literature permeability data and to predict the permeability variation among all the flow regions with different gas pressures and pore sizes. The results illustrate that the flow types may change within a specific pore (especially for small pores) during the pressure depletion in shale gas production and different gas transport mechanisms may coexist for some shales with typical bimodal pore size distribution under a specific gas pressure condition. This work provides a unified way to analyse gas flow behaviours and gas permeability variation in different regions and can be readily applied to the reservoir simulation models to predict the gas production behaviour especially the long term gas production behaviour.

Although permeability data for different gas shales have been reported previously and attempts have been made to match permeability with empirical correlations, theoretical studies of shale permeability modelling are lacking. In this work, the correlation between fracture permeability and effective stress is established for gas shales through theoretical derivation. This model is able to match the permeability data for different gas shales. The matching results for the gas shale studied show that the model coefficient, fracture compressibility, which decreases as initial shale permeability increases, is strongly affected by the flow directions and varies with the shale’s mineralogical composition. Furthermore, the correlation between fracture permeability and reservoir pressure has also been established. Sensitivity study shows that fracture permeability may decrease significantly with the reservoir pressure drawdown. Moreover, the horizontal fracture permeability drop is found to be significantly affected by the Young’s modulus’ anisotropic ratio ( E h / E v ). The insights gained warrant further theoretical and experimental studies to evaluate shale fracture permeability.

The non-Darcy factor, an indicator for the non-Darcy effect, is dependent on the properties of porous media and pore fluid including permeability, viscosity, density, flow velocity and a coefficient named as β factor. Experimental results show that the β factor can be expressed as a power law of permeability. For conventional gas reservoirs, this β factor can be assumed as a constant as the permeability change is negligible. However, the constant β factor may not be suitable for coal seams with remarkable permeability change and a variable β factor as a function of coal permeability should be an alternative. Moreover, the coal permeability change is complex due to the competing effects of coal cleat compression and sorption induced coal shrinkage/swelling. Few studies have been done previously to incorporate the variable β factor as a function of coal permeability in reservoir simulations. In the present work, both the coal permeability change and the variable β factor are coupled in a dual porosity model to study the non-Darcy flow behavior in coal seams. The simulation results illustrate that the evolution of non-Darcy factor becomes tortuous by using a variable β factor, which differs from the monotonic behavior when constant β factors are applied. Furthermore, increasing the coal cleat compressibility and matrix shrinkage strain tends to intensify the tortuous behavior. The simulation results also indicate that using typical constant β factors, instead of the variable one, may significantly underestimate or overestimate the gas production rate for coalbed methane wells.

A remote-control tether-less isolation tool is a mechanical device that is normally used in pipelines to block the flow at a given position by transforming a blocking module. In this study, the interactions between the fluid and the plug module of the isolation tool were investigated. Simulations of the plug process and particle image velocimetry measurements were performed to study the flow characteristics. Numerical solutions for the continuity, momentum, and energy equations were obtained by using commercial software based on finite-volume techniques. Box–Behnken design was applied, and response surface methodology (RSM)-based CFD simulation analysis was conducted. The dynamic model in the plug process was built by RSM and used to evaluate the influences of the main mechanical parameters on the pressure during the plug process. The diameter of the isolation tool and the diameter of the plug module have strong influences on the process, and the length of the isolation tool has only a little influence on the plug process.