Microseismic interpretation

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Microseismic interpretation is the monitoring of small scale microseismic signals made from either natural or man made tremors. Most widely used to estimate stimulated reservoir volumes and in identifying major fault or fracture events for use in hydrocarbon exploration. Since its advent around the year 2000, microseismic interpretation/monitoring (MSI/MSM) has greatly increased the geological understanding of fracture propagation, especially in that of hydrocarbon reservoirs. What this article will be going over are the main steps in the quality control evaluation of the acquired microseismic data. [1]



The first step in MSI is figuring out the level of uncertainty regarding the event location. In short the uncertainty value is just a general area of where the microseismic location is, give or take a bit of area in regards to finding out in which direction the microseismic waves traveled, and how big the resultant fracture is and in what direction did it fracture. Location uncertainty will depend upon the Signal to Noise Ratio, the higher the SNR the lower the level of uncertainty is and the lower the SNR the higher the level of uncertainty is (refer to the first uppermost graph on the right), the level of uncertainty can be represented by an ellipsoid (second graph on lower right side where the green colored ellipsoid is the area of error) depicting a three dimensional view of the uncertainty (depth, distance, azimuth).[2][3]

Uncertainty Modifiers

  • Filtering : By making certain cutoff points to the signal to noise ratio values, we can decrease the uncertainty by prematurely getting rid of higher location uncertainty values that will most likely introduce errors into the interpretation.[3]
  • Velocity Model: The velocity model can introduce major errors in interpretation but is generally passed upon due to in a majority of cases errors in the velocity model being only noticed during the final portion of the interpretation.[3]

Observation Well Bias

Regarding the location at the site of the observation well with respect to the treatment well and the location of microseismic activity, the microseismic interpretation can encounter some errors. These errors are due to distance of the sensor arrays, ie. if the sensor arrays are very close to the microseismic location then only small events can be detected, if the sensors are too far way then even the largest activity cannot be detected. To minimize this error we can determine the Viewing Distance, Distance Bias, Viewing Azimuth, and the Nodal Planes.[3]

  • Viewing distance : By evaluating the magnitude of the events vs distance from the sensor arrays, we can determine if the geometry of the fracture was mapped.
  • Distance Bias : The event patterns must be corrected for the distance, to do this we can just normalize a magnitude-distance graph and filter the event patterns at the proper magnitude cutoff point.
  • Viewing Azimuth : The azimuth of the observation well with respect to the event patterns can result in significant differences in the microseismic patterns.
  • Nodal Planes : The radiation patterns given off by microseismic events can result in blind spots, as a result of this a P/SH graph is used to identify where these blind spots are to see if the nodal planes might be affecting the detection of the microseismic event.

Geologic Environment

MSI varies greatly on the geology of the area of interest where the fracture occurs. The event pattern depends greatly on reservoir fluids, stress regime, existence of natural fractures, matrix permeability, and rock properties of the area, the general trend is that by knowing a good deal of the geology of the area the interpretation of the microseismic can be constrained even more such that we have less room for error.[3]

Visualization & Integration

By integrating all of the geologic, seismic, treatment, and event pattern data we can start the interpretation process and we can use many of the interpretation tools that are listed below to use for evaluating our data.[3]

  • Temporal and spatial evaluation - Observing how event patterns change over time can give insight into treatment behavior.
  • Stimulated volume - If fracture growth becomes extremely complex then the usage of the temporal and spatial evaluation of the stimulated volume data in conjunction with the fracture treatment data can better provide a more quantitative method of stimulation behavior.
  • Event histograms and event count - When integrating the histograms of the event count with the fracture treatment data a better understanding of the stimulation behavior is gained.
  • Frequency-magnitude relationship - Not in all cases but by using the logarithmic distribution of magnitudes one can graphically see and separate hydraulic fracture growth from fault activation.
  • Failure orientation - By using P/SH wave graphs the orientation of the shear failure can be evaluated if it exhibited a predominant orientation.
  • Anomalies - Anomalous behavior can be caused by many different complex interactions involving the hydraulic fracture, the point being that it's important to understand said interactions because they lead to insight regarding the overall interpretation.

Mass Balance and Simple Fracture Mechanics

By including the volume of fluid and the type of proppant pumped in addition to the net pressure of the fluid we can further constrain our interpretation, by including simple fracture mechanic equations to find the average fracture width, total fracture area, and total fracture length from the previously mentioned information. Essentially what we can do with mass balance calculations and simple fracture mechanics equations is that we can get a better picture of the fracture geometry and how that same fracture geometry is linked towards the well production as shown in the graph below.[4][5][6]


References/Work Citations

  1. Maxwell, S. C., Shemeta, J. E., Campbell, E., & Quirk, D. J. (2008, January 1). Microseismic Deformation Rate Monitoring. Society of Petroleum Engineers. https://doi.org/10.2118/116596-MS
  2. Maxwell, S. C. (2009, January 1). Assessing the Impact of Microseismic Location Uncertainties On Interpreted Hydraulic Fracture Geometries. Society of Petroleum Engineers. https://doi.org/10.2118/125121-MS
  3. 3.0 3.1 3.2 3.3 3.4 3.5 Cipolla, C. L., Mack, M. G., Maxwell, S. C., & Downie, R. C. (2011, January 1). A Practical Guide to Interpreting Microseismic Measurements. Society of Petroleum Engineers. https://doi.org/10.2118/144067-MS
  4. L. Cipolla, Craig & John Williams, Michael & Weng, Xiaowei & Gavin Mack, Mark & Maxwell, S. (2010). Hydraulic Fracture Monitoring to Reservoir Simulation: Maximizing Value. Society of Petroleum Engineers - International Petroleum Technology Conference 2012, IPTC 2012. 4. 10.2118/133877-MS.
  5. L. Cipolla, Craig. (2009). Modeling Production and Evaluating Fracture Performance in Unconventional Gas Reservoirs. Journal of Petroleum Technology - J PETROL TECHNOL. 61. 84-90. 10.2118/118536-MS.
  6. L. Cipolla, Craig & Lolon, Elyezer & J. Mayerhofer, Michael. (2009). Reservoir Modeling and Production Evaluation in Shale-Gas Reservoirs. International Petroleum Technology Conference. 10.2523/13185-MS.