Inverted diffuser for abrasive slurry flow with sensor for internal damages

Abstract

The present disclosure is for an inverted diffuser usable in wellheads and other wellsite equipment. The inverted diffuser includes at least one first section having a first vertical inner surface, a first inclined inner surface relative to the first vertical inner surface, and one or more channels supporting one or more releasable fasteners between the at least one first section and a wall of a wellhead or of a wellsite equipment. At least one second section is press-fitted or fastened adjacent to the at least one first section. A method for application of the inverted diffuser in a wellhead and in other wellsite equipment is also disclosed, along with a method of manufacture of the inverted diffuser.

What is claimed is: 
     
         1 . A system to be used with an inverted diffuser having a plurality of sections associated to a wall of a wellhead using releasable fasteners or press-fitting, the system comprising:
 pressure monitoring sub-system to monitor the inverted diffuser;   at least one processor; and   memory storing instructions that, when executed by the at least one processor, cause the system to:
 provide a multi-node neural network that is trained using calibration information that is associated with the pressure monitoring sub-system; and 
 determine, using the multi-node neural network and information from the pressure monitoring sub-system, that a leak has occurred with respect to the inverted diffuser or that at least one of the plurality of sections is to be replaced; and 
 provide an indication from the system for replacement of the at least one of the plurality of sections. 
   
     
     
         2 . The system of  claim 1 , wherein the calibration information is associated with different leaks through one or more channel gaps of the plurality of sections. 
     
     
         3 . The system of  claim 1 , wherein the pressure monitoring sub-system is to determine pressure or pressure changes that are outside a threshold or that do not satisfy the threshold with respect to plurality of sections, wherein the calibration information is associated with at least part of the pressure or pressure changes that are outside the threshold or that do not satisfy the threshold. 
     
     
         4 . The system of  claim 1 , wherein the at least one processor and the memory are provided at a well site or provided remotely from the well site, the well site comprising the inverted diffuser and the pressure monitoring sub-system. 
     
     
         5 . The system of  claim 1 , wherein the at least one processor comprises multi-processor capabilities to train and to test the multi-node neural network to correlate pressure associated with the calibration information and with degradation information gathered over at least predetermined cycles of operation of the inverted diffuser. 
     
     
         6 . The system of  claim 1 , wherein the pressure monitoring sub-system comprises pressure sensors to monitor a channel associated with bolts of the plurality of sections. 
     
     
         7 . The system of  claim 1 , wherein computational fluid dynamics (CFD) simulations indicating an erosion rate for the inverted diffuser and for abrasive slurries at a determined flow rate is associated with the calibration information to train the multi-node neural network. 
     
     
         8 . At least one processor to comprise a multi-node neural network that is trained using calibration information that is associated with a pressure monitoring sub-system of an inverted diffuser having a plurality of sections associated to a wall of a wellhead using releasable fasteners or press-fitting, wherein the multi-node neural network to determine that a leak has occurred with respect to the inverted diffuser or to determine that at least one of the plurality of sections is to be replaced based in part on information from the pressure monitoring sub-system, and wherein the at least one processor is to provide an indication for replacement of the at least one of the plurality of sections. 
     
     
         9 . The at least one processor of  claim 8 , wherein the calibration information is associated with different leaks through one or more channel gaps of the plurality of sections. 
     
     
         10 . The at least one processor of  claim 8 , wherein the calibration information is associated with at least part of pressure or pressure changes that are outside a threshold or that do not satisfy the threshold with respect to plurality of sections, as determined using the pressure monitoring sub-system. 
     
     
         11 . The at least one processor of  claim 8 , wherein the processor is provided at a well site or provided remotely from the well site, the well site comprising the inverted diffuser and the pressure monitoring sub-system. 
     
     
         12 . The at least one processor of  claim 8 , further comprising multi-processor capabilities to train and to test the multi-erode neural network to correlate pressure associated with the calibration information and degradation information gathered over at least predetermined cycles of operation of the inverted diffuser. 
     
     
         13 . The at least one processor of  claim 8 , wherein the calibration information is provided from the pressure monitoring sub-system using pressure sensors that monitor a channel associated with bolts of the plurality of sections. 
     
     
         14 . The at least one processor of  claim 8 , wherein computational fluid dynamics (CFD) simulations indicating an erosion rate for the inverted diffuser and for abrasive slurries at a determined flow rate is associated with the calibration information to train the multi-node neural network. 
     
     
         15 . A method to be used with an inverted diffuser having a plurality of sections associated to a wall of a wellhead using releasable fasteners or press-fitting, the method comprising:
 monitoring the inverted diffuser using a pressure monitoring sub-system;   providing a multi-node neural network that is trained using calibration information that is associated with the pressure monitoring sub-system;   determining, using the multi-node neural network, that a leak has occurred with respect to the inverted diffuser or at least one of the plurality of sections is to be replaced; and   providing an indication for replacement of the at least one of the plurality of sections to a processor-based system.   
     
     
         16 . The method of  claim 15 , further comprising:
 changing the at least one of the plurality of sections based in part on the indication in the processor-based system.   
     
     
         17 . The method of  claim 15 , wherein the calibration information is associated with different leaks through one or more channel gaps of the plurality of sections. 
     
     
         18 . The method of  claim 15 , wherein the pressure monitoring sub-system is to determine pressure or pressure changes that are outside a threshold or that do not satisfy the threshold with respect to plurality of sections, wherein the calibration information is associated with at least part of the pressure or pressure changes that are outside the threshold or that do not satisfy the threshold. 
     
     
         19 . The method of  claim 15 , further comprising:
 providing a processor-based system with the multi-node neural at a well site or remotely from the well site, wherein the well site comprises the inverted diffuser and the pressure monitoring sub-system.   
     
     
         20 . The method of  claim 15 , further comprising:
 training the multi-node neural network to correlate pressure associated with the calibration information and with degradation information gathered over at least predetermined cycles of operation of the inverted diffuser; and   testing the multi-node neural network using information from the monitoring of the inverted diffuser.