Loss of drilling fluid is a common problem during the drilling of wells and it restricts the appropriate functionality of muds.Drilling fluid loss significantly increases drilling costs and non-productive time as well...Loss of drilling fluid is a common problem during the drilling of wells and it restricts the appropriate functionality of muds.Drilling fluid loss significantly increases drilling costs and non-productive time as well as the drilling operation risks.Various investigations have been carried out in order to find appropriate mud additives that either block fractures and pores or reduce fluid loss by improving the fluid rheology.Cheap,environmentally friendly and effective additives are still required by the drilling industry.Hence,the application of available materials in each region,to produce appropriate additives,is a challenge for the oil industry.In this study,Eucalyptus Camaldulensis(EUC)bark powder has been chosen as a new,fibrous,cheap,environmentally friendly and available material to control fluid loss,particularly in southern Iran.Different characterization tests,such as acid dissolution and fluid loss control,were carried out to study the performance of the new proposed additive.Removal by hydrochloric acid and sulfuric acid were studied at various acid concentrations and temperatures.Dynamic fluid loss was also measured at different EUC concentrations.Our study showed that EUC powder can reduce the final fluid loss by 88-97%,the initial fluid loss by 45-66%,and the total loss by 87e94%,which is a satisfactory level.展开更多
Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production exp...Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production expenses. This research utilizes the H oilfield as an example, employs seismic features to analyze mud loss prediction, and produces a complete set of pre-drilling mud loss prediction solutions. Firstly, 16seismic attributes are calculated based on the post-stack seismic data, and the mud loss rate per unit footage is specified. The sample set is constructed by extracting each attribute from the seismic trace surrounding 15 typical wells, with a ratio of 8:2 between the training set and the test set. With the calibration results for mud loss rate per unit footage, the nonlinear mapping relationship between seismic attributes and mud loss rate per unit size is established using the mixed density network model.Then, the influence of the number of sub-Gausses and the uncertainty coefficient on the model's prediction is evaluated. Finally, the model is used in conjunction with downhole drilling conditions to assess the risk of mud loss in various layers and along the wellbore trajectory. The study demonstrates that the mean relative errors of the model for training data and test data are 6.9% and 7.5%, respectively, and that R2is 90% and 88%, respectively, for training data and test data. The accuracy and efficacy of mud loss prediction may be greatly enhanced by combining 16 seismic attributes with the mud loss rate per unit footage and applying machine learning methods. The mud loss prediction model based on the MDN model can not only predict the mud loss rate but also objectively evaluate the prediction based on the quality of the data and the model.展开更多
文摘Loss of drilling fluid is a common problem during the drilling of wells and it restricts the appropriate functionality of muds.Drilling fluid loss significantly increases drilling costs and non-productive time as well as the drilling operation risks.Various investigations have been carried out in order to find appropriate mud additives that either block fractures and pores or reduce fluid loss by improving the fluid rheology.Cheap,environmentally friendly and effective additives are still required by the drilling industry.Hence,the application of available materials in each region,to produce appropriate additives,is a challenge for the oil industry.In this study,Eucalyptus Camaldulensis(EUC)bark powder has been chosen as a new,fibrous,cheap,environmentally friendly and available material to control fluid loss,particularly in southern Iran.Different characterization tests,such as acid dissolution and fluid loss control,were carried out to study the performance of the new proposed additive.Removal by hydrochloric acid and sulfuric acid were studied at various acid concentrations and temperatures.Dynamic fluid loss was also measured at different EUC concentrations.Our study showed that EUC powder can reduce the final fluid loss by 88-97%,the initial fluid loss by 45-66%,and the total loss by 87e94%,which is a satisfactory level.
基金the financially supported by the National Natural Science Foundation of China(Grant No.52104013)the China Postdoctoral Science Foundation(Grant No.2022T150724)。
文摘Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production expenses. This research utilizes the H oilfield as an example, employs seismic features to analyze mud loss prediction, and produces a complete set of pre-drilling mud loss prediction solutions. Firstly, 16seismic attributes are calculated based on the post-stack seismic data, and the mud loss rate per unit footage is specified. The sample set is constructed by extracting each attribute from the seismic trace surrounding 15 typical wells, with a ratio of 8:2 between the training set and the test set. With the calibration results for mud loss rate per unit footage, the nonlinear mapping relationship between seismic attributes and mud loss rate per unit size is established using the mixed density network model.Then, the influence of the number of sub-Gausses and the uncertainty coefficient on the model's prediction is evaluated. Finally, the model is used in conjunction with downhole drilling conditions to assess the risk of mud loss in various layers and along the wellbore trajectory. The study demonstrates that the mean relative errors of the model for training data and test data are 6.9% and 7.5%, respectively, and that R2is 90% and 88%, respectively, for training data and test data. The accuracy and efficacy of mud loss prediction may be greatly enhanced by combining 16 seismic attributes with the mud loss rate per unit footage and applying machine learning methods. The mud loss prediction model based on the MDN model can not only predict the mud loss rate but also objectively evaluate the prediction based on the quality of the data and the model.
