Hydraulic fracturing framework

1 . A method comprising:

disposing a distributed acoustic sensing (DAS) system in an observation well;

generating a mesh model of a fracture network including preexisting natural fractures in a subsurface geologic region based on geological data acquired by the DAS system;

performing a first stimulation operation from a stimulation well with a perforator at a first position in the stimulation well to cause a first propagation of a first hydraulic fracture in the subsurface geologic region toward the observation well;

recording a DAS signature via the DAS system in the observation well in real-time with the first propagation of the first hydraulic fracture;

performing, via a modeling framework, a comparison between the geological data and simulation results generated using the mesh model of the subsurface geologic region and enrichment equations that represent discontinuities in the subsurface geologic region, wherein the performing the comparison comprises utilizing the modeling framework comprising a forward model and an inversion model, the inversion model configured to:

compute a strain rate signature at the observation well as impacted by distances and orientations of the preexisting natural fractures;

create first templates for the strain rate signature as impacted by the distances;

create second templates for the strain rate signature as impacted by the orientations; and

combine the first templates and the second templates to describe combinations of strain rate signatures for arbitrary distances and arbitrary orientations of the preexisting natural fractures; and

inferring updated orientations, distances, and densities of the preexisting natural fractures from the inversion model;

characterizing the subsurface geologic region by updating the mesh model based on the inferring;

moving the perforator to a second position in the stimulation well based on the updating the mesh model; and

performing a second stimulation operation via the perforator in the second position in the stimulation well to cause a second propagation of a second hydraulic fracture that short circuits the stimulation well and a neighboring well.

2 . The method of claim 1 , wherein the geological data comprise strain data acquired using a cable in the observation well.

3 . The method of claim 2 , wherein the observation well is offset from the stimulation well.

4 . The method of claim 3 , wherein the observation well is substantially parallel to the stimulation well.

5 . The method of claim 1 , wherein the enrichment equations represent the first propagation of the first hydraulic fracture as one of the discontinuities.

6 . The method of claim 1 , wherein the enrichment equations represent natural fractures in the subsurface geologic region as a number of the discontinuities.

7 . The method of claim 1 , wherein performing the comparison compares the geological data and the simulation results with respect to time.

8 . The method of claim 7 , wherein the geological data change with respect to time due to the first propagation of the first hydraulic fracture and due to orientation of at least one of the discontinuities.

9 . The method of claim 1 , wherein the geological data depend on orientation of at least one natural fracture in the subsurface geologic region.

10 . The method of claim 1 , wherein performing the comparison comprises using a trained machine learning model, wherein the trained machine learning model is trained using the simulation results.

11 . The method of claim 1 , wherein performing the comparison comprises matching a pattern of the geological data to a pattern of the simulation results.

12 . The method of claim 1 , wherein the characterizing comprises determining an orientation of at least one of the discontinuities.

13 . The method of claim 1 , wherein the first propagation of the first hydraulic fracture propagates outwardly from the stimulation well, and wherein the characterizing comprises determining an angle of at least one natural fracture with respect to a longitudinal axis of the stimulation well.

14 . The method of claim 1 , comprising, based at least in part on the characterizing the subsurface geologic region, controlling a hydraulic fracturing operation.

15 . The method of claim 14 , wherein the first propagation of the first hydraulic fracture occurs responsive to performing the hydraulic fracturing operation.

16 . The method of claim 1 , comprising, based at least in part on the characterizing the subsurface geologic region, planning a hydraulic fracturing operation for propagation of another hydraulic fracture.

17 . The method of claim 1 , further comprising:

generating a stimulation operations roadmap in real-time with the performing the comparison between the geological data and the simulation results; and

the performing the second stimulation operation is based on the stimulation operations roadmap.

18 . The method of claim 1 , wherein the forward model is configured to model an effect of the preexisting natural fractures on a low-frequency DAS signature recorded via the DAS system in the observation well.

19 . A system comprising:

a processor;

a memory operatively coupled to the processor;

processor-executable instructions stored in the memory and executable to instruct the system to:

dispose a distributed acoustic sensing (DAS) system in an observation well;

generate a mesh model of a fracture network including preexisting natural fractures in a subsurface geologic region based on geological data acquired by the DAS system;

perform a first stimulation operation from a stimulation well with a perforator at a first position in the stimulation well to cause a first propagation of a first hydraulic fracture in the subsurface geologic region toward the observation well;

record a DAS signature via the DAS system in the observation well in real-time with the first propagation of the first hydraulic fracture;

perform, via a modeling framework, a comparison between the geological data and simulation results generated using the mesh model of the subsurface geologic region and enrichment equations that represent discontinuities in the subsurface geologic region, wherein the performing the comparison comprises utilizing the modeling framework comprising a forward model and an inversion model, the inversion model configured to:

compute a strain rate signature at the observation well as impacted by distances and orientations of the preexisting natural fractures;

create first templates for the strain rate signature as impacted by the distances;

create second templates for the strain rate signature as impacted by the orientations; and

combine the first templates and the second templates to describe combinations of strain rate signatures for arbitrary distances and arbitrary orientations of the preexisting natural fractures; and

infer updated orientations, distances, and densities of the preexisting natural fractures from the inversion model;

characterize the subsurface geologic region by updating the mesh model based on the inferring;

move the perforator to a second position in the stimulation well based on the updating the mesh model; and

perform a second stimulation operation via the perforator in the second position in the stimulation well to cause a second propagation of a second hydraulic fracture that short circuits the stimulation well and a neighboring well.

20 . One or more non-transitory computer-readable storage media comprising processor-executable instructions executable by a system to instruct the system to:

dispose a distributed acoustic sensing (DAS) system in an observation well;

generate a mesh model of a fracture network including preexisting natural fractures in a subsurface geologic region based on geological data acquired by the DAS system;

perform a first stimulation operation from a stimulation well with a perforator at a first position in the stimulation well to cause a first propagation of a first hydraulic fracture in the subsurface geologic region toward the observation well;

record a DAS signature via the DAS system in the observation well in real-time with the first propagation of the first hydraulic fracture;

perform, via a modeling framework, a comparison between the geological data and simulation results generated using the mesh model of the subsurface geologic region and enrichment equations that represent discontinuities in the subsurface geologic region, wherein the performing the comparison comprises utilizing the modeling framework comprising a forward model and an inversion model, wherein the forward model is configured to model an effect of the preexisting natural fractures on a low-frequency DAS signature recorded via the DAS system in the observation well and wherein the inversion model is configured to:

compute a strain rate signature at the observation well as impacted by distances and orientations of the preexisting natural fractures;

create first templates for the strain rate signature as impacted by the distances;

create second templates for the strain rate signature as impacted by the orientations; and

combine the first templates and the second templates to describe combinations of strain rate signatures for arbitrary distances and arbitrary orientations of the preexisting natural fractures;

infer updated orientations, distances, and densities of the preexisting natural fractures from the inversion model;

characterize the subsurface geologic region by updating the mesh model based on the inferring;

move the perforator to a second position in the stimulation well based on the updating the mesh model;

perform a second stimulation operation via the perforator in the second position in the stimulation well to cause a second propagation of a second hydraulic fracture that short circuits the stimulation well and a neighboring well;

generate a stimulation operations roadmap in real-time with the performing the comparison between the geological data and the simulation results; and

the performing the second stimulation operation is based on the stimulation operations roadmap.