This paper demonstrates the applicability of machine learning algorithms in sand production problems with natural gas hydrate (NGH)-bearing sands, which have been regarded as a grave concern for commercialization. The sanding problem hinders the commercial exploration of NGH reservoirs. The common sand production prediction methods need assumptions for complicated mathematical derivations. The main contribution of this paper was to introduce machine learning into the prediction sand production by using data from laboratory experiments. Four main machine learning algorithms were selected, namely, K-Nearest Neighbor, Support Vector Regression, Boosting Tree, and Multi-Layer Perceptron. Training datasets for machine learning were collected from a sand production experiment. The experiment considered both the geological parameters and the sand control effect. The machine learning algorithms were mainly evaluated according to their mean absolute error and coefficient of determination. The evaluation results showed that the most accurate results under the given conditions were from the Boosting Tree algorithm, while the K-Nearest Neighbor had the worst prediction performance. Considering an ensemble prediction model, the Support Vector Regression and Multi-Layer Perceptron could also be applied for the prediction of sand production. The tuning process revealed that the Gaussian kernel was the proper kernel function for improving the prediction performance of SVR. In addition, the best parameters for both the Boosting Tree and Multi-Layer Perceptron were recommended for the accurate prediction of sand production. This paper also involved one case study to compare the prediction results of the machine learning models and classic numerical simulation, which showed the capability of machine learning of accurately predicting sand production, especially under stable pressure conditions.

As the natural gas hydrate (NGH) resources show a bright future, more and more commercial and technical focuses have been devoted to this area. The low productivity, the sanding problem, and the poor economic performance are vital problems hindering the long‐term commercial exploration and development of NGS sediments. Among all these problems, the sanding problem can aggravate the low productivity and the poor economic efficiency problems. Therefore, it is necessary to investigate and tackle the sanding problem for the safe, long‐term, and large‐scale commercial development of offshore NGH resources. The sanding problem of the NGH sediment is highly related to the mechanical behavior of sediment. The main influencing factors are discussed, which are hydrate saturation, effective confining pressure, sand content, hydrate distribution, and multiple‐physical fields. This article summarizes the current research achievements systematically to determine how these factors affect sand production in NGH sediments. Besides the macrolevel about mechanical behaviors of NGH sediment, the sand motion modeling is also included in this review. The source of sand production is the free‐moving sand which comes from formation deformation. The review compares most commonly used yield criteria and then recommends a proper one for NGH sediments. The paper subsequently discusses the common investigation methods for the sanding problem in this area, including numerical simulation and experiments. The design and effect of sand control techniques have also been reviewed and discussed. According to the review results, the paper concludes the current research drawbacks and generates suggestions for future research. The novel methods of sand control investigation are machine learning and optimization methods.

This paper numerically investigates the flow-induced vibration of a circular cylinder attached with front and/or rear splitter plates at a low Reynolds number of Re = 120. The effects of plate length and plate location on the hydrodynamic coefficient, vibration response, and flow wake are examined and discussed in detail. The results reveal that the hydrodynamic coefficient of the cylinder with a single rear plate is significantly reduced at U_r ≤ 8 (U_r is the reduced velocity), resulting in the vortex-induced vibration (VIV) suppression. Nevertheless, the galloping is excited at U_r > 8 due to the hydrodynamic instability, accompanied by the jump of response amplitude and hydrodynamic force, as well as the abrupt drop of response frequency. The alternate reattachment of shear layers on the plate surface introduces an extra lift force that strengthens the vibration response. By introducing an individual front plate, significant VIV suppression is achieved. The vibration exhibits variable patterns when the cylinder is equipped with bilateral plates, including the typical VIV mode, weak VIV-galloping coupling mode, and IB-galloping-DB mode (IB and DB represent the initial branch and desynchronization branch of VIV, respectively). The galloping branch in IB-galloping-DB mode is observed with an abrupt drop in response frequency, as well as a tiny time lag between the displacement and lift force. The vibration response is significantly suppressed when the cylinder is simultaneously equipped with a 1D front plate and a 1–2D rear plate due to the streamlined profile.

Drilling wells with gas, or gas-drilling, has recently been adopted to drill unconventional oil and gas reservoirs including tight sands, gas shale and oil shale. However, the performance of gas-drilling is very unpredictable in many areas due to lack of optimization of drilling parameters. Because gas properties are greatly affected by temperature, a reliable thermal model is required for gas-drilling optimization. Such a model is not available and this paper fills the gap. An analytical thermal-model was derived in this study for predicting bottom hole gas temperature under various flow conditions. The result given by the analytical model was compared with that by an existing approximation, showing an accuracy improvement of 14%. Sensitivity analysis with the new model indicates that formation fluid influx dominates the temperature profile inside and outside the drill string, while Joule-Thomson cooling and drill cutting's intrusion affect temperature only at bottom hole. Applications of the model are demonstrated in an example. This paper provides an analytical tool to drilling engineers for optimizing their gas-drilling operations.