Using artificial intelligence methods to exploit well logging data
A method for constructing a high fidelity logging while drilling (LWD) log that includes obtaining a temporal well depth log and a plurality of temporal LWD logs from a drilling operation. The method further includes obtaining a temporal record of operational drilling parameters from the drilling operation. The method further includes determining, using a first machine-learning model, a temporal history of the drilling operation and constructing, using the temporal well depth log and the plurality of temporal LWD logs, a plurality of temporal property logs at each depth in the plurality of depths. The method further includes processing the plurality of temporal property logs with, at least, a second machine-learning model to form a plurality of corrected temporal property logs and aggregating the plurality of corrected temporal property logs to form a plurality of high fidelity LWD logs.
1 . A method, comprising:
obtaining a temporal well depth log and a plurality of temporal logging while drilling (LWD) logs from a drilling operation, wherein the drilling operation comprises a drill bit traversing through a subsurface at a plurality of depths;
obtaining a temporal record of operational drilling parameters from the drilling operation;
determining, using a first machine-learning model, a temporal history of the drilling operation, wherein the first machine-learning model accepts, at least in part, the temporal well depth log and the temporal record of operational drilling parameters;
constructing, using the temporal well depth log and the plurality of temporal LWD logs, a plurality of temporal property logs at each depth in the plurality of depths, wherein each temporal property log comprises a plurality of data points;
identifying and removing outlier data points from each of the temporal property logs based on the temporal history;
processing the plurality of temporal property logs with, at least, a second machine-learning model to form a plurality of corrected temporal property logs;
aggregating the plurality of corrected temporal property logs to form a plurality of high fidelity LWD logs; and
determining a lithology of the subsurface using, at least in part, the plurality of high fidelity LWD logs.
2 . The method of claim 1 , further comprising:
characterizing a plurality of formation properties of the subsurface with respect to a passage of time using the plurality of corrected temporal property logs.
3 . The method of claim 1 , further comprising:
generating a hydrocarbon quick-look log depicting a time-lapse invasion of drilling fluid.
4 . The method of claim 1 , further comprising:
determining, using the temporal well depth log, a velocity profile of the drill bit and processing the plurality of high fidelity LWD logs with a locally adaptive filter, wherein the locally adaptive filter is adapted according to the velocity profile.
5 . The method of claim 1 , wherein
the temporal history comprises temporally ordered drilling events as classified by the first machine-learning model.
6 . The method of claim 5 , wherein a drilling event is one or more of the following: a drill string connection event, a drilling fluid circulation event, a pause in drilling activity.
7 . The method of claim 1 , wherein the first machine-learning model and second machine-learning model are deep neural networks.
8 . A non-transitory computer-readable memory comprising computer-executable instructions stored thereon that, when executed on a processor, cause the processor to perform:
receive a temporal well depth log and a plurality of temporal logging while drilling (LWD) logs from a drilling operation, wherein the drilling operation comprises a drill bit traversing through a subsurface at a plurality of depths;
receive a temporal record of operational drilling parameters from the drilling operation;
determine, using a first machine-learning model, a temporal history of the drilling operation, wherein the first machine-learning model accepts, at least in part, the temporal well depth log and the temporal record of operational drilling parameters;
construct, using the temporal well depth log and the plurality of temporal LWD logs, a plurality of temporal property logs at each depth in the plurality of depths, wherein each temporal property log comprises a plurality of data points;
identify and remove outlier data points from each of the temporal property logs based on the temporal history;
process the plurality of temporal property logs with, at least, a second machine-learning model to form a plurality of corrected temporal property logs;
aggregate the plurality of corrected temporal property logs to form a plurality of high fidelity LWD logs; and
determine a lithology of the subsurface using, at least in part, the plurality of high fidelity LWD logs.
9 . The non-transitory computer-readable memory of claim 8 , further comprising computer-executable instructions for:
characterizing a plurality of formation properties of the subsurface with respect to a passage of time using the plurality of corrected temporal property logs.
10 . The non-transitory computer-readable memory of claim 8 , further comprising computer-executable instructions for:
generating a hydrocarbon quick-look log depicting an invasion of drilling fluid with respect to a passage of time.
11 . The non-transitory computer-readable memory of claim 8 , further comprising computer-executable instructions for:
determining, using the temporal well depth log, a velocity profile of the drill bit and processing the plurality of high fidelity LWD logs with a locally adaptive filter, wherein the locally adaptive filter is adapted according to the velocity profile.
12 . The non-transitory computer-readable memory of claim 8 , wherein
the temporal history comprises temporally ordered drilling events as classified by the first machine-learning model.
13 . The non-transitory computer-readable memory of claim 8 , wherein a drilling event is one or more of the following: a drill string connection event, a drilling fluid circulation event, a pause in drilling activity.
14 . The non-transitory computer-readable memory of claim 8 , wherein the first machine-learning model and second machine-learning model are deep neural networks.
15 . A system, comprising:
a well site performing a drilling operation, wherein the drilling operation comprises a drill bit traversing through a subsurface;
a plurality of logging while drilling (LWD) tools configured to measure properties of the subsurface;
a temporal record of operational drilling parameters of the well site;
a temporal well depth log;
a plurality of temporal LWD logs, one for each LWD tool, where each temporal LWD log comprises measurements of an associated property of the subsurface over a period of time;
a first machine-learning model;
a second machine-learning model; and
a computer, comprising:
one or more computer processors and a non-transitory computer-readable memory storing computer-executable instructions that when executed on the one or more computer processors cause the one or more compute processors to perform:
receiving the temporal well depth log and the plurality of temporal logging while drilling (LWD) logs;
receiving the temporal record of operational drilling parameters;
determining, using the first machine-learning model, a temporal history of the drilling operation, wherein the first machine-learning model accepts, at least in part, the temporal well depth log and the temporal record of operational drilling parameters;
constructing, using the temporal well depth log and the plurality of temporal LWD logs, a plurality of temporal property logs at each depth in the plurality of depths, wherein each temporal property log comprises a plurality of data points;
identifying and removing outlier data points from each of the temporal property logs based on the temporal history;
processing the plurality of temporal property logs with, at least, the second machine-learning model to form a plurality of corrected temporal property logs;
aggregating the plurality of corrected temporal property logs to form a plurality of high fidelity LWD logs; and
determining a lithology of the subsurface using, at least in part, the plurality of high fidelity LWD logs.
16 . The system of claim 15 , wherein the non-transitory computer-readable further comprises computer-executable instructions for:
characterizing a plurality of formation properties of the subsurface with respect to a passage of time using the plurality of corrected temporal property logs.
17 . The system of claim 15 , wherein the non-transitory computer-readable further comprises computer-executable instructions for:
generating a hydrocarbon quick-look log depicting an invasion of drilling fluid with respect to a passage of time.
18 . The system of claim 15 , wherein the non-transitory computer-readable further comprises computer-executable instructions for:
determining, using the temporal well depth log, a velocity profile of the drill bit and processing the plurality of high fidelity LWD logs with a locally adaptive filter, wherein the locally adaptive filter is adapted according to the velocity profile.
19 . The system of claim 15 , wherein
the temporal history comprises temporally ordered drilling events as classified by the first machine-learn model.
20 . The system of claim 15 , wherein the first machine-learned model and second machine-learned model are deep neural networks.