# US 11,905,818 B2 · Deep learning methods for wellbore pipe inspection > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,905,818 B2 - Document ID: US11905818B2 - Status: Granted - Kind: B2 - Application number: 17/114,712 - Filed: 2020-12-08 - Published: 2022-06-09 - Granted: 2024-02-20 - Earliest claimed: 2020-12-08 - Assignees: Halliburton Energy Services, Inc. - Applicants: Halliburton Energy Services, Inc. - Inventors: Ahmed Elsayed Fouda; Junwen Dai; Li Pan - Web page: https://patentgrep.com/patent/US11905818B2 ## CPC - E21B 47/0025: {generating an image of the borehole wall using down-hole measurements, e.g. acoustic or electric} - G06N 3/04: Architecture, e.g. interconnection topology - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/08: Learning methods - G06N 3/09: Supervised learning - G06N 3/0985: Hyperparameter optimisation; Meta-learning; Learning-to-learn - G06T 3/4046: using neural networks - G06T 7/0004: {Industrial image inspection} - E21B 47/006: {Detection of corrosion or deposition of substances} - E21B 2200/22: Fuzzy logic, artificial intelligence, neural networks or the like - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30108: Industrial image inspection - E21B: EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract Methods and systems for inspecting the integrity of multiple nested tubulars are provided. A method for inspecting the integrity of multiple nested tubulars can comprise conveying an electromagnetic pipe inspection tool inside the innermost tubular of the multiple nested tubulars; taking measurements of the multiple nested tubulars with the electromagnetic pipe inspection tool; inverting the measurements for a tubular integrity property of each individual tubular of the multiple nested tubulars to provide inverted tubular integrity properties; arranging the inverted integrity properties into an inverted image representative of an estimated tubular integrity property of each individual tubular; and feeding the inverted image to a pre-trained deep neural network (DNN) to produce a corrected image, wherein the DNN comprises at least one convolutional layer, and wherein the corrected image comprises a representation of the tubular integrity property of each individual tubular of the multiple nested tubulars. ## Claims ### Claim 1 A method for inspecting tubular integrity comprising: conveying an electromagnetic pipe inspection tool inside an innermost tubular of multiple nested tubulars, wherein the electromagnetic pipe inspection tool has one or more transmitters and one or more receivers; taking measurements of the multiple nested tubulars with the electromagnetic pipe inspection tool; inverting the measurements for a tubular integrity property of each individual tubular of the multiple nested tubulars to provide inverted tubular integrity properties; arranging the inverted tubular integrity properties into an inverted image representative of an estimated tubular integrity property of each individual tubular; and feeding the inverted image to a pre-trained deep neural network (DNN) to produce a corrected image, wherein the DNN comprises at least one convolutional layer, and wherein the corrected image comprises a representation of a corrected tubular integrity property of each individual tubular of the multiple nested tubulars. ### Claim 2 (depends on 1) The method of claim 1, wherein the inversion is based on a radial one-dimensional (R1D) inversion model. ### Claim 3 (depends on 2) The method of claim 2, further comprising correcting, via the DNN, inversion artifacts introduced by the R1D inversion model. ### Claim 4 (depends on 1) The method of claim 1, wherein taking measurements of the multiple nested tubulars with the electromagnetic pipe inspection tool comprises transmitting electromagnetic fields at one or more frequencies with the one or more transmitters; and measuring at least one of a real-part, an imaginary-part, an absolute, an amplitude, and a phase of a received signal at the one or more frequencies with the one or more receivers. ### Claim 5 (depends on 1) The method of claim 1, wherein taking measurements of the multiple nested tubulars with the electromagnetic pipe inspection tool comprises exciting the multiple nested tubulars with pulsed electromagnetic fields with the one or more transmitters; and measuring a decay response of the pulsed electromagnetic fields in the time domain with the one or more receivers. ### Claim 6 (depends on 1) The method of claim 1, wherein the tubular integrity property comprises a cross-sectional thickness, a magnetic permeability, an electrical conductivity, or a combination thereof. ### Claim 7 (depends on 1) The method of claim 1, wherein a value assigned to each pixel in the corrected image is proportional to a percentage change of the tubular integrity property of each of the individual tubulars of the multiple nested tubulars from a nominal tubular integrity property of each of the individual tubulars of the multiple nested tubulars. ### Claim 8 (depends on 1) The method of claim 1, wherein feeding the inverted image to the pre-trained DNN comprises splitting the inverted image into sections based on depth. ### Claim 9 (depends on 1) The method of claim 1, wherein the pre-trained DNN further comprises at least one of a concatenation layer, a summation layer, a max pooling layer, an up-sampling layer, and a dense layer. ### Claim 10 (depends on 1) The method of claim 1, further comprising training the DNN to provide the pre-trained DNN, wherein training the DNN comprises building a database by using at least one of measurements of known cases and simulation, wherein the database includes a plurality of samples, and wherein each sample of the plurality of samples comprises a true image of the tubular integrity property of each of the individual tubulars of the multiple nested tubulars and a corresponding inverted image. ### Claim 11 (depends on 10) The method of claim 10, wherein training the DNN further comprises finding optimum network parameters to minimize a misfit between output images produced by the DNN and corresponding true images according to an error metric. ### Claim 12 (depends on 1) The method of claim 1, further comprising training the DNN to provide the pre-trained DNN, wherein training the DNN comprises building a database by using at least one of measurements of known cases and simulation, and finding optimum hyperparameters for inversion, wherein the database includes a plurality of samples, and wherein each sample of the plurality of samples comprises a true image of the tubular integrity property of each of the individual tubulars of the multiple nested tubulars and corresponding raw measurements of the multiple nested tubulars. ### Claim 13 (depends on 1) The method of claim 1, wherein taking measurements of the multiple nested tubulars with the electromagnetic pipe inspection tool comprises taking azimuthal measurements of the multiple nested tubulars using the electromagnetic pipe inspection tool. ### Claim 14 (depends on 13) The method of claim 13, wherein the inverted image comprises a three-dimensional (3D) inverted image, and wherein the 3D inverted image has depth as a first dimension, azimuth as a second dimension, and the inverted tubular integrity property as a third dimension. ### Claim 15 (depends on 13) The method of claim 13, wherein the corrected image comprises a 3D representation of the tubular integrity property of each the individual tubulars of the multiple nested tubulars. ### Claim 16 (depends on 13) The method of claim 13, wherein the convolutional layer comprises a convolutional filter, and wherein the convolutional filter is 3D filter. ### Claim 17 One or more non-transitory computer-readable media comprising program code for inspecting tubular integrity, the program code to: initiate measurements of multiple nested tubulars with an electromagnetic pipe inspection tool conveyed inside an innermost tubular of the multiple nested tubulars; perform an inversion on the measurements for a tubular integrity property of each individual tubular of the multiple nested tubulars to provide inverted tubular integrity properties; arrange the inverted tubular integrity property into an inverted image representative of an estimated tubular integrity property of each individual tubular; and feed the inverted image to a pre-trained DNN to produce a corrected image, wherein the DNN comprises at least one convolutional layer, and wherein the corrected image comprises a representation of a corrected tubular integrity property of each individual tubular of the multiple nested tubulars. ### Claim 18 (depends on 17) The computer-readable media of claim 17, wherein the tubular integrity property comprises a cross-sectional thickness, a magnetic permeability, an electrical conductivity, or a combination thereof. ### Claim 19 (depends on 17) The computer-readable media of claim 17, wherein a value assigned to each pixel in the corrected image is proportional to a percentage change of the tubular integrity property of each of the multiple nested tubulars from a nominal tubular integrity property of each of the multiple nested tubulars. ### Claim 20 A system comprising: an electromagnetic pipe inspection tool disposed inside an innermost tubular of multiple nested tubulars; a pre-trained DNN comprising at least one convolutional layer; a processor; and a computer-readable medium having program code executable by the processor to: initiate measurements of the multiple nested tubulars with the electromagnetic pipe inspection tool conveyed inside the innermost tubular; perform an inversion on the measurements for a tubular integrity property of each individual tubular of the multiple nested tubulars to provide inverted tubular integrity properties; arrange the inverted tubular integrity properties into an inverted image representative of an estimated tubular integrity property of each individual tubular; and feed the inverted image to the pre-trained DNN to produce a corrected image, wherein the corrected image comprises a representation of a corrected tubular integrity property of each individual tubular of the multiple nested tubulars.