Cluster efficiency evaluation using strain observation from a monitoring well
AssigneeSchlumberger Technology Corporation
InventorsDmitry Sergeevich Kuznetsov, Roman Vladimirovich Korkin, Mikhail Kaznacheev, Anna Zhirkina, Joel Herve Le Calvez
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Abstract
A comprehensive methodological framework for monitoring hydraulic fracturing operations utilizing the surface and/or downhole pumping data (with pressure, rate and proppant concentration), fracture growth model from a fracture simulator coupled with low-frequency strain data acquired via Distributed Acoustic Sensing (DAS) technology.
Claims20
A method for evaluating and optimizing cluster efficiency in a hydraulic fracturing operation, comprising:
- (a) simulating a plurality of hydraulically-stimulated fracture scenarios using at least one analytical or semi-analytical model using data from at least one formation and stimulation parameters of interest;
- (b) generating, for each simulated scenario, a predicted strain signal over time using a strain rate simulation model, wherein a strain is calculated for a fiber-optic cable disposed in an offset monitoring well matching a real case monitoring geometry;
- (c) identifying, for each scenario, a first detectable signal appearance time ti at which the simulated distributed acoustic sensing signal becomes visible in the offset well;
- (d) initiating a hydraulic fracturing treatment job and acquiring real-time distributed acoustic sensing data from the monitoring well;
- (e) determining a real-time signal appearance time t* in the acquired distributed acoustic sensing data;
- (f) comparing t* to a set of first detectable signal appearance times {ti} to select at least one best-matching scenario;
- (g) selecting a primary matching scenario as a reference case for ongoing monitoring;
- (h) updating the reference case in real-time based on a comparison between the ongoing distributed acoustic sensing data and predicted strain profiles of the selected scenarios;
- and
- (i) generating an output indicating a most probable cluster efficiency configuration and corresponding hydraulically-induced fracture development parameters, including uncertainty quantification of these parameters.
A system for evaluating cluster efficiency in a hydraulic fracturing operation, comprising:
- (a) a modeling module configured to simulate hydraulic fracture propagation under a plurality of cluster configurations;
- (b) a strain simulation engine configured to predict distributed acoustic sensing strain signals for each configuration;
- (c) a signal monitoring module configured to acquire real-time distributed acoustic sensing data from an offset well;
- (d) a data transfer protocol to ensure all data are transferred in real-time;
- (e) a system enabling all data to interact;
- (f) a pattern recognition module configured to identify a matching simulated scenario based on real-time signal data;
- and
- (g) a decision engine configured to determine fracture growth characteristics and recommend optimal cluster efficiency based on matching results.
A computer-readable medium storing instructions which, when executed by one or more processors, cause a system to perform a method for evaluating and optimizing cluster efficiency in a hydraulic fracturing operation, the method comprising:
- (a) simulating a plurality of hydraulically-stimulated fracture scenarios using at least one analytical or semi-analytical model using data from the formations and stimulation parameters of interest;
- (b) generating, for each simulated scenario, a predicted strain signal over time using a strain rate simulation model, wherein a strain is calculated for a fiber-optic cable disposed in an offset monitoring well matching a real case monitoring geometry;
- (c) identifying, for each scenario, a first detectable signal appearance time ti at which the simulated distributed acoustic sensing signal becomes visible in the offset monitoring well;
- (d) initiating a hydraulic fracturing treatment job and acquiring real-time distributed acoustic sensing data from the monitoring well;
- (e) determining a real-time signal appearance time t* in the acquired distributed acoustic sensing data;
- (f) comparing t* to a set of simulated times {ti} to select one or more best-matching scenarios;
- (g) selecting a primary matching scenario as a reference case for ongoing monitoring;
- (h) updating the reference case in real-time based on a comparison between the ongoing distributed acoustic sensing data and predicted strain profiles of the selected scenarios;
- and
- (i) generating an output indicating a most probable cluster efficiency configuration and corresponding hydraulically-induced fracture development parameters, including uncertainty quantification of these parameters.
Dependencies are inferred from the claim text.