Conocophillips Company

Baishali Roy

Ge Jin

Kyle R Krueger

Kyle Friehauf

Dana M Jurick

Herbert W Swan

Charles Clinton Mosher

Ali Can Tura

Kevin T Raterman

Seth Busetti

Helen E Farrell

Raymond R Reid, Jr

Frank P Filice

Mark Elliott Willis

Robert G Keys

Michael Paul Davidson

Jesse J Constantine

Samik Sil

Douglas J Foster

Darryl G Buswell

Kevin Mendoza

A method of optimizing production of a hydrocarbon-containing reservoir by measuring low-frequency Distributed Acoustic Sensing (LFDAS) data in the well during a time period of constant flow and during a time period of no flow and during a time period of perturbation of flow and simultaneously measuring Distributed Temperature Sensing (DTS) data from the well during a time period of constant flow and during a time period of no flow and during a time period of perturbation of flow. An initial model of reservoir flow is provided using the LFDAS and DTS data; the LFDAS and DTS data inverted using Markov chain Monte Carlo method to provide an optimized reservoir model, and that optimized profile utilized to manage hydrocarbon production from the well and other asset wells.

Production logging inversion based on LFDAS/DTS

Monitoring and diagnosing completion during hydraulic fracturing operations provides insights into the fracture geometry, inter-well frac hits and connectivity. Conventional monitoring methods (microseismic, borehole gauges, tracers, etc.) can provide a range of information about the stimulated rock volume but may often be limited in detail or clouded by uncertainty. Utilization of DAS as a fracture monitoring tool is growing, however most of the applications have been limited to acoustic frequency bands of the DAS recorded signal. In this paper, we demonstrate some examples of using the low-frequency band of Distributed Acoustic Sensing (DAS) signal to constrain hydraulic fracture geometry. DAS data were acquired in both offset horizontal and vertical monitor wells. In horizontal wells, DAS data records formation strain perturbation due to fracture propagation. Events like fracture opening and closing, stress shadow creation and relaxation, ball seat and plug isolation can be clearly identified. In vertical wells, DAS response agrees well with co-located pressure and temperature gauges, and illuminates the vertical extent of hydraulic fractures. DAS data in the low-frequency band is a powerful attribute to monitor small strain and temperature perturbation in or near the monitor wells. With different fibered monitor well design, the far-field fracture length, height, width, and density can be accurately measured using cross-well DAS observations.

Low frequency distributed acoustic sensing hydraulic fracture geometry

A system for measuring fluid flow in a wellbore is provided. A probe includes at least a heater. A fiber optic cable is connected to the probe. The system is programmed to perform operations including: changing an output of the heater to thereby change a temperature of drilling fluid moving over a fiber optic cable; measuring a strain on the fiber optic cable caused by changing the temperature of the drilling fluid; preliminarily determining a velocity of the drilling fluid from the measured strain; measuring at least a second parameter of the drilling fluid; adjusting the preliminary determined velocity based on the measured at least a second parameter to yield an adjusted velocity; and determining a flow rate of the drilling fluid based on the adjusted velocity.

Passive production logging instrument using heat and distributed acoustic sensing

Various aspects described herein relate to a machine learning based detecting of fracture hits in offset monitoring wells when designing hydraulic fracturing processes for a particular well. In one example, a computer-implemented method includes receiving a set of features for a first well proximate to a second well, the second well undergoing a hydraulic fracturing process for extraction of natural resources from underground formations; inputting the set of features into a trained neural network; and providing, as output of the trained neural network, a probability of a fracture hit at a location associated with the set of features in the first well during a given completion stage of the hydraulic fracturing process in the second well.

Machine-learning based fracture-hit detection using low-frequency DAS signal

Methods and systems for assessing cross-well interference and/or optimizing hydrocarbon production from a reservoir by obtaining low frequency DAS and DTS data and pressure data from a monitor well, when both the monitor and production well are shut-in, and then variably opening the production well for production, and detecting the temperature and pressure fluctuations that indication cross-well interference, and localizing the interference along the well length based on the low frequency DAS data. This information can be used to optimize well placement, completion plans, fracturing plans, and ultimately optimize production from a given reservoir.

Low frequency DAS well interference evaluation

A data acquisition program, which includes core, image log, microseismic, DAS, DTS, and pressure data, is described. This program can be used in conjunction with a variety of techniques to accurately monitor and conduct well stimulation.

Stimulated rock volume analysis

Production logging inversion based on DAS/DTS

Monitoring and diagnosing completion during hydraulic fracturing operations provides insights into the fracture geometry, inter-well frac hits and connectivity. Conventional monitoring methods (microseismic, borehole gauges, tracers, etc.) can provide a range of information about the stimulated rock volume but may often be limited in detail or clouded by uncertainty. Utilization of DAS as a fracture monitoring tool is growing, however most of the applications have been limited to acoustic frequency bands of the DAS recorded signal. Low-frequency Distributed Acoustic Sensing (DAS) signal is used to constrain hydraulic fracture geometry. Events like fracture opening and closing, stress shadow creation and relaxation, ball seat and plug isolation can be clearly identified.

A method of assessing cross-well interference and/or optimizing hydrocarbon production from a reservoir by obtaining low frequency DAS and DTS data and pressure data from a monitor well, when both the monitor and production well are shut-in, and then variably opening the production well for production, and detecting the temperature and pressure fluctuations that indication cross-well interference, and localizing the interference along the well length based on the low frequency DAS data. This information can be used to optimize well placement, completion plans, fracturing plans, and ultimately optimize production from a given reservoir.

A workflow using techniques for improving signal-to-noise ratio and decreasing interferences for Low-Frequency Distributed Acoustic Sensing is described.

Low-frequency DAS SNR improvement

The invention relates to DAS observation has been proven to be useful for monitoring hydraulic fracturing operations. While published literature has shown focus on the high-frequency components (&#x3e;1 Hz) of the data, this invention discloses that much of the usable information may reside in the very low frequency band (0-50 milliHz). Due to the large volume of a DAS dataset, an efficient workflow has been developed to process the data by utilizing the parallel computing and the data storage. The processing approach enhances the signal while decreases the data size by 10000 times, thereby enabling easier consumption by other multi-disciplinary groups for further analysis and interpretation. The polarity changes as seen from the high signal to noise ratio (SNR) low frequency DAS images are currently being utilized for interpretation of completions efficiency monitoring in hydraulically stimulated wells.

Low frequency distributed acoustic sensing

Tools and methods for monitoring a subterranean formation is provided. Methods for monitoring include: creating a time-lapse azimuth stack between an azimuth stack on a first seismic survey and an azimuth stack on a second seismic survey; identifying a lowest root mean square energy and a highest root mean square energy for each time-lapse azimuth stack; and recording an azimuth with largest overall root mean square energy.

Time-lapse 4D scattering for imaging hydraulically induced fractures

This disclosure describes a method of combining DAS and DTS data to accurately estimate borehole temperature. The described method takes advantage of the thermal sensitivity of DAS signal in the low-frequency band, and combines with the absolute temperature measurement from DTS, to produce a distributed temperature estimation that is up to 10000 more accurate than the current commercial solution. The DAS and DTS data should be record simultaneously at the same well. The DAS data are first low-pass filtered and then converted into temperature variation measurement. Then an accurate temperature estimation is obtained by fitting both DTS and DAS data.

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Houston, TX, United States of America

Baishali Roy