No abstract

No abstract

Shear wave velocity (VS) data from sedimentary rock sequences is a prerequisite for implementing most mathematical models of petroleum engineering geomechanics. Extracting such data by analyzing finite reservoir rock cores is very costly and limited. The high cost of sonic dipole advanced wellbore logging service and its implementation in a few wells of a field has placed many limitations on geomechanical modeling. On the other hand, shear wave velocity VS tends to be nonlinearly related to many of its influencing variables, making empirical correlations unreliable for its prediction. Hybrid machine learning (HML) algorithms are well suited to improving predictions of such variables. Recent advances in deep learning (DL) algorithms suggest that they too should be useful for predicting VS for large gas and oil field datasets but this has yet to be verified. In this study, 6622 data records from two wells in the giant Iranian Marun oil field (MN#163 and MN#225) are used to train HML and DL algorithms. 2072 independent data records from another well (MN#179) are used to verify the VS prediction performance based on eight well-log-derived influencing variables. Input variables are standard full-set recorded parameters in conventional oil and gas well logging data available in most older wells. DL predicts VS for the supervised validation subset with a root mean squared error (RMSE) of 0.055 km/s and coefficient of determination (R2) of 0.9729. It achieves similar prediction accuracy when applied to an unseen dataset. By comparing the VS prediction performance results, it is apparent that the DL convolutional neural network model slightly outperforms the HML algorithms tested. Both DL and HLM models substantially outperform five commonly used empirical relationships for calculating VS from Vp relationships when applied to the Marun Field dataset. Concerns regarding the model's integrity and reproducibility were also addressed by evaluating it on data from another well in the field. The findings of this study can lead to the development of knowledge of production patterns and sustainability of oil reservoirs and the prevention of enormous damage related to geomechanics through a better understanding of wellbore instability and casing collapse problems. Graphical abstract

<abstract> <p>Polymers have been used for many years to control the mobility of injected water and increase the rate of oil extraction from unconventional reservoirs. Polymer flossing improves the volume of the broom, reduces the finger effect, creates channels, and delays water breakage. The combination of these processes has the potential to increase oil production and reduce production costs. To carry out this process, various polymers are used alone or in combination with surfactants and alkalis. In this study, a new type of polymer called FLOPPAM 3630 has been used to investigate the overload of very heavy oil reservoirs. For this purpose, six polymer solutions with different concentrations were made, and stability tests on shear rate, time, and temperature were performed. The polymer's stability results indicate that it is stable under other shear rate, temperature, and time passage conditions. As a result, this polymer is a suitable candidate for conducting silicification tests in reservoir temperature conditions. Then three more suitable polymer solutions were selected, and the polymer was polished. The results showed that the solution with a concentration of 1000 ppm has the best yield of about 40%. The reason for the good efficiency of this concentration is that the surface and vertical sweepers are higher than the other concentrations. Also, the difference in efficiency between less than 1000 and 2000 ppm is greater because it is more economical, and its injectability is easier to use with less concentration. Furthermore, the oil efficiency of this type of polymer in sandblasting is higher than that of other polymers tested under these conditions, making its use more economical.</p> </abstract>