The main difference between “Surface MS Monitoring”, “Downhole MS Monitoring”, and “DAS MS Monitoring” goes back to the sensitivity and the frequency band of the instrumentation used for monitoring.

Conventional Surface vs. Downhole MS Monitoring

First let’s compare the Surface vs. Downhole MS Monitoring using 3C geophones (call it conventional): Although surface MS processing (mainly via stacking and migration) is often quicker than downhole data processing (first break picking), the downhole monitoring geophones (usually 15 Hz) provides a wider range of detectability in the reservoir since they resolve smaller size events. However, the 15 Hz geophones are unable to give accurate magnitude estimates for larger events (> Mw -1) due to saturation. That’s where the surface MS acquisition comes in handy using broadband geophones with main frequency cutoff as low as 0.01 Hz that can pick up very low frequency events (> Mw -1 and above).   

Conventional Downhole vs. DAS MS Monitoring

In the case of DAS MS monitoring, since the sensitivity of fiber is lower than downhole geophones the acquired dataset is usually sparse (i.e., fewer events are detected), therefore it provides less information about the dynamic reservoir behavior through advanced analytics. To improve sensitivity, signals from multiple points along the fiber can be stacked but this results in a decrease in MS location accuracy. Using DAS for MS is also restrictive in determining characteristics of the microseismic events. Despite the charm of simpler deployment of Fiber for hydraulic fracturing, more R&D work is needed for this technique to arrive at scientific maturity for MS monitoring.

Combining Monitoring Methods

The downhole monitoring for hydraulic fracturing always wins if you have to choose one method over another (conditional to creating appropriate coverage). If you are combining Surface MS and DAS MS monitoring, you should know that both these techniques will provide you with a dataset that is biased toward larger magnitude events occurring closer to the treatment wells because that’s what the instruments are capable of resolving. Meanwhile, the Surface MS Monitoring is complementary to conventional Downhole since the surface network extends the recording range of seismicity generated during a stimulation. The cost of adding a sparse surface network is generally minimal and is something that should be considered for many plays.

If you are in a decision-making position, budgeting for DAS and MS surveys, you need to take as many details as possible into consideration to arrive at the monitoring solution that works best for your asset. Don’t underestimate the value of feasibility studies (modeling) before you make a monitoring service purchase decision to reduce the unexpected surprises with the acquired MS data.

Ellie Ardakani, CEO @ Meta

Above you are visualizing such modeling using 5 surface sensors in the metaKinetic platform.

For sure there are more aspects into these techniques that must be considered. Want to have access to this simulation and explore by yourself or have further questions? Contact us!

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Check out our expanded abstract published by SEG (Technical Program 2018) or contact us to get a copy.

The post Using machine learning to estimate the flow of stress from recorded microseismicity during hydraulic fracturing appeared first on Meta | Innovative AI Analytics and Training Software.

We have developed a unique approach to understand the influence of fractures on fluid flow and production from impermeable reservoirs and evaluate completion effectiveness. We characterize the microseismicity-derived discrete fracture network in a North American shale-gas reservoir using modified scanline and topology methods. Using concepts of node and branch classification and assessing the number of connections (fracture intersections), the network connectivity is established volumetrically. The zones of permeability enhancement are then identified using the connection per branch and line (CB and CL), tied to percolation thresholds of the fracture system. These zones consist of a primary zone with a high proportion of doubly connected fractures, a secondary zone populated with partially connected fractures, and a tertiary or unstimulated zone dominated by isolated fractures. These divisions are reflected in the deformation that is observed in the reservoir as measured through a cluster-based description of the microseismicity. The primary and secondary zones are considered spanning fracture clusters, and they take part in production, whereas the tertiary zone is recognized as nonspanning fractures, and though it may enhance the bulk permeability of the rock mass, it is unlikely to contribute to reservoir production.

Check out our full article published in SEG Interpretation Journal, Volume 6, Issue 2 or contact us to get a copy.

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Check out the expanded abstract published by CSEG (Geoconvention 2018) or contact us to get a copy.

The post Investigation of the impact of depleted zones and completion sequence on hydraulic fracturing performance using microseismic collective behaviour analysis appeared first on Meta | Innovative AI Analytics and Training Software.

Effective hydraulic fracturing in unconventional shale reservoirs requires an understanding of the pre-existing discontinuities (impermeable/not effectively connected natural fractures) state and an assessment of whether at any point of the stimulation it is energetically favourable to move fluids within the reservoir. The dynamic nature of the local stress regime owing to hydraulic fracturing leads to stimulation of different fracture sets which could include bedding-parallel fractures and bedding planes. Describing the progression of the fracturing into the formation and ancillary issues of how far into the reservoir the rock has been stimulated and the stimulation containment provides the opportunity for operators to potentially control fracture behaviour and improve completion designs.

Evaluation of hydraulic fracturing generated microseismicity through seismic moment tensor inversion and further stress inversion techniques, where there is high signal-to-noise signals registered with a sufficient angular distribution of sensors around the events, can resolve the orientations of the fractures on which these ruptures occur. This process then identifies the predominance of fracture types, fracture orientations and dimensions within the stimulated reservoir.

The comparison of fracture network associated with a stimulation of different stages we can establish the importance of different fracture sets in effectively constraining the stimulation to the zone of interest. This information further allows operators to establish the stimulation effectiveness if production data is available.

Check out our full article published in First Break Journal Vol 35, No 4, April 2017 or contact us to get a copy.

The post The influence of bedding-parallel fractures in hydraulic fracture containment appeared first on Meta | Innovative AI Analytics and Training Software.

While event distributions tend to be the first and most frequently examined aspect of a microseismic monitoring effort, because the generation of a microseismic event is not immediately diagnostic of fluid-induced fracturing, the event clouds tend to overestimate the effective area of fracturing. In order to gain further insight into how microseismic events describe effective fracture growth, a deeper look at the waveforms through techniques like Seismic Moment Tensor Inversion (SMTI) and subsequent stress inversion can be effective. These steps are necessary to describe the discrete network of cracks, from the microseismic data. Using a fracture network topology approach, the network can then be characterized in terms of its ability to percolate fluids.

We compare how cracks behave for a regular geometric shot cluster (GSC) and a variable shot cluster (VSC) and assess variations in the stimulations. Both shot clusters were completed in consecutive stages of the same lateral. The mechanisms from the GSC stages show shear-dominant mechanisms with opening and closing components in roughly equal proportions, while the VSC stages have a higher concentration of shear-tensile opening failures. Furthermore, the GSC stages showed modest connectivity around the treatment well relative to the VSC stages, which showed significant growth of connected fractures away from the treatment well. Since the VSC stages also showed relatively more stable stress behaviour than the GSC stages, these observations suggest that stability in stresses allows for steady growth of the fracture network across the reservoir.

This type of higher-order analysis of microseismic data is critical to establishing value from this data stream in terms of completion evaluation. The recognition that each microseismic event is tied to the rupture of a crack in the reservoir allows for these types of comparisons to be made in a robust fashion and be tied to the underlying geomechanics that governs the type of response from one type of completion to the other.

Check out our full article published in SPE (Hydraulic Fracturing Technology Conference 2018) or contact us to get a copy.

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Using the example of seismic moment tensor inversion (SMTI) data, we describe an approach for obtaining a picture of a connected fracture network that can further be described in terms of the percolation properties of the network. This allows for the moment tensor data to be linked to where the hydraulic stimulation fractures connect to the treatment well and therefore the volumes where we may expect production.
Further consideration of microseismic event clusters can identify the different deformation processes that accompany the microseismicity. By clustering events of similar character, and considering both how they are distributed in time and space, as well as the insights into their failure processes from a detailed study of their source mechanics, the deformation in the reservoir
can be mapped. Characterizing the deformation by the degree of co-seismic (anelastic) deformation allows the processes in the reservoir to be described in terms of different deformation indexes, ‘dynamic parameters’: plasticity index (PI) corresponding to anelastic deformation; stress index (SI) as related to the localized stress behaviour/conditions leading to seismicity; and diffusion index (DI) which describes the rate of stress transfer as it results in seismicity throughout the volume of interest.

We introduce the site and give an overview to the microseimsic data acquisition for a lateral well completion in the STACK play (Sooner Trend Anadarko basin Canadian and Kingfisher counties). We then describe an approach for processing these data, through moment tensor inversion, into a picture of the Discrete Fracture Network (DFN). This requires a methodology to group events occurring under like stress conditions to invert for the stress ratio and the principal stress axes, such that the fracture planes may be deterministically derived from the moment tensor data. We also discuss the methodology to determine the cluster-based dynamic parameters. We then illustrate how we can use these tools to arrive at an integrated interpretation of processes occurring during the hydraulic completion, and how these data can be used to affect design decisions for completion and field development.

Check out our full article published in EAGE First Break, Volume 35, Number 12, or contact us to get a copy.

The post Integration of SMTI topology with dynamic parameter analysis to characterize fracture-connectivity related to flow and production along wellbores in the STACK play appeared first on Meta | Innovative AI Analytics and Training Software.

We connect these concepts of fluid-driven vs stress-triggered seismicity to volumes in the reservoir of different percolation potentials. Fluid-driven processes are of primary importance to tying the microseismicity to productive volume, but we suggest that the stress-induced processes may also play a significant role in identifying poorly- or well-connected crack networks and hence the stimulated volumes within the reservoir. As such, we can resolve the likely volumes of primary (initial) and secondary (longer-term) production through these clustering processes and ensuring the behaviors determined are consistent with more of a fluid-induced vs stress-triggered behavior. This ranking of volumes in terms of productivity is analogous to work done in predicting variations in enhanced permeability in different engineering workflows, with the added benefit of being able to show variability along the well. As such, we suggest that coupling the dynamic parameter response to estimating and ranking the geometries of volumes of different productivity provides a rigorous methodology to tie microseismicity to stimulated reservoir volume, allowing for credible predictions of accessible reserves to be made over a short timescale.

Check out our full article published by Unconventional Resources Technology Conference (URTEC-2697672-MS) or contact us to get a copy.

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