Prediction of Cement Slurry Density Based on AMIndRNN
DOI:
https://doi.org/10.54097/ajst.v7i1.11303Keywords:
Cementing Operation, Cement Slurry Density, Attention Mechanism, Independently Recurrent Neural Network (IndRNN).Abstract
Cement slurry density is one of the key factors affecting the consolidation effect of oil casing and wellbore, and its value has a direct impact on the quality of cementing and construction safety. In the traditional cementing operation, the professionals perform experiments based on information from adjacent Wells to obtain fuzzy cement density, and then constantly adjust the final cement density in the actual operation, which costs a lot of labor and time, and is not good for real-time operation. In response to the above problems,This paper proposes AMIndRNN(Attention Mechanism combined with Independently Recurrent Neural Network) for cement slurry density prediction, and optimizes IndRNN by introducing SMU activation function. The comparison experiment between AMIndRNN model and baseline model shows that AMIndRNN model has obvious advantages in various performance indexes, which can be used to guide the design of actual cement slurry density.
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