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Sand production in oil and gas wells can occur if fluid flow exceeds a certain threshold governed by factors such as consistency of the reservoir rock, stress state and the type of completion used around the well. The amount of solids can be less than a few grams per cubic meter of reservoir fluid, posing only minor problems, or a substantial amount over a short period of time, resulting in erosion and in some cases filling and blocking of the wellbore. This paper provides a review of selected approaches and models that have been developed for sanding prediction. Most of these models are based on the continuum assumption, while a few have recently been developed based on discrete element model. Some models are only capable of assessing the conditions that lead to the onset of sanding, while others are capable of making volumetric predictions. Some models use analytical formulae, particularly those for estimating the onset of sanding while others use numerical models, particularly in calculating sanding rate. Although major improvements have been achieved in the past decade, sanding tools are still unable to predict the sand mass and the rate of sanding for all field problems in a reliable form.

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Flexible pressure sensors have attracted considerable research interest and efforts owing to their broad application prospects in wearable devices, health monitoring, and human−machine interfacing. High-sensitivity, wide-workable-range, and low-cost pressure sensors are the primary requirement in practical application. In this work, flexible pressure sensors with high sensitivity in a wide pressure range are constructed by introducing a knoll-like microstructured surface into a percolative thermoplastic polyurethane/carbon black sensitive film, using a facile, efficient, and cost-effective screen-printing route. The prepared pressure sensors exhibit an ultrawide sensing pressure range of 0−1500 kPa, high sensitivity (5.205 kPa −1 in the range of 0−100 kPa and 0.63 kPa −1 over 1200 kPa), fast response, and excellent durability for more than 30 000 cycles. We demonstrated the applications of our pressure sensors in health monitoring, such as detection of wrist radial artery pulse waves, phonation, and vibrations. In addition, the proposed sensors showed the potential in object manipulation and human−machine interfacing, capable of detecting spatial pressure distribution, measuring grip forces, and monitoring gas pressures.

We performed a comprehensive sand prediction study of several deep, HPHT wells within a large reservoir and the findings revealed that, for these wells, common criteria based on critical drawdown, minimum bottomhole pressure, depletion or fluid velocity failed to predict the outcome by a relatively large margin. All these wells were subjected to relatively high levels of drawdown and also very high fluid velocities and, with the exception of one well, none showed any sanding until water production was encountered. In this paper, we provide a rationale for why water can be highly effective in inducing sand production and we support our argument using advanced numerical modeling. This exercise also ranks the performance of some of the common tools and theories that are conventionally used for sand prediction. We also provide reasons why some of these models do not perform satisfactorily for the cases studied. The originality of the work is in demonstrating that prior to sand production, the dis-aggregated rock (i.e., individual sand particles) around the wellbore is basically held together by the capillary tension which is destroyed by water flow. While the capillary tension appears to be insignificant (as it is in the order of 1 psi or so), it provides a significant resistance against sand mobilization. The importance of this issue has been quantified using advanced numerical modeling. This concept is vastly different from the previous theories that propose water weakens the rock through chemical interaction or changing the relative permeability. Introduction While a great deal of work has been done in the general area of sand production1–15, approaches used to quantify the volumes of the produced sand have faced challenges in the validation process (Class A prediction). In fairness, it is difficult to firmly single out the deficiencies when predictions do not materialize in a consistent fashion as the quality of the input data, monitoring of sanding events as well as the assumptions and physics used for modeling sanding can all be potential culprits. Following up on this line of reasoning, it is not the intention of this paper to prove or refute any previous work done in sand production studies nor to show that our method is universally superior. The primary intention is to provide a deeper insight into the mechanisms of sanding, in general, and water-production induced sanding, in particular. We try to support the views presented using basic fundamentals along with field observations. Significance and Potential Applications Conventional sand production models2,3,15–17 predict the onset of sanding which in practice is presumed to signify large-scale sanding. This single case solution scenario does not give operators options to assess risks and benefits which is becoming increasingly more relevant under the currently optimized completion and production practices. In essence, operators would like to know, at any stage in a well's life, how much sand will be produced (rate and duration) for a given production strategy (e.g., maximum drawdown, effects of bean-up and shut-in cycles, impact of water). By better understanding the role of various variables one is enabled to choose the optimal completion method for the life of the well (which may exclude installation or deferring sand control measures) and quantify the impact of aggressive fluid production strategies in terms of volume and rate of sanding.

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