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  • Francisco Caycedo

Understanding and Predicting Horizontal Well Performance in a Shale Gas Reservoir


This post is about:


  • Correlations that try to explain / predict horizontal well performance (IP and EUR).


  • A demonstration on how critical is the horizontal well placement (Stratigraphically) and its impact on productivity.


  • A methodology used to predict petrophysical properties in the horizontal wells.


  • The Ability to detect faults and its negative impact on productivity.


  • The Integration of Geology, Seismic Information, Petrophysics, Completions, Production, and Reservoir Engineering data.

Unconventional resource plays, like shale gas has become an important source of natural gas, especially in North America, increasing the need of better characterize these reservoirs, improve reserves estimations, production efficiency, and mitigate associated risks.


In continuation of the previous post (Quick Shale Gas Petrophysics Review), this one shows a specific example of the integration of the petrophysical characterization with the data form other disciplines.


Prior to this work, we believed that the position of the well within the reservoir was not important because at the end of the day the fracking “connected” all the layers.


A total of 57 wells with 947 fracking stages in 6 pads were included in the analysis and the main motivation for this work was to understand the well performance variations in the field, as can be seen in the picture below.


Detailed Petrophysical analysis was performed in all the Pilot (vertical wells for most of the pads) and control wells, integrating the full suite of logs and lab measurements from drilling cuttings.





The picture to the right shows an example of one of the pilot wells. In the fist track is the Gamma Ray log, in the second track the lithology, in the third the raw porosity logs (Sonic, Neutron and Density/PEF), in the fourth track the effective porosity, and in the last track the Poisson Ratio.



Some intervals with very low effective porosity, high clay content, and high Poisson Ratio are highlighted with red arrows.










After completing the detailed characterization of the vertical wells, a Proprietary methodology and in-house developed software allowed to stratigraphically locate the horizontal well trajectory within the reservoir and predict (extrapolate) its petrophysical properties, specifically the effective porosity that was called Synthetic Porosity.


In the example presented in the picture, an effective porosity curve has been predicted (extrapolated) to the horizontal well. The same methodology is applied to all the horizontal wells in the field using the pilot well in each pad or the closer control well, and the average porosity value in the fractured intervals is computed for each well.



Finally a good correlation between the average synthetic porosity times the number of fracking stages and the Initial Production was found. It is clear that there are too many parameters involved in the well performance (like well spacing/interference, fracking type, fracking volumes, number of clusters, closure stress, faulting, etc.), but these two can be used to explain 70% of the total variation in IP.


It is important to understand that in this plot, lower average Phi doesn't necessarily mean that the porosity in the area or pad is worse, but that the wells were placed in the layers with lower porosity.



Low End Example: WELL-1


The well presented in the picture below is one of the wells with poorer performance in the field. It can be appreciated that most of its trajectory is located in an interval with low effective porosity (higher clay content and higher Poisson Ratio). When computing the average synthetic porosity in the fractured intervals, the result is a low number (2.1%) that multiplied by the number of fracking stages also results in a low number and this correlates with a low IP.



Intermediate Example: WELL-2


The well presented in the picture below is one of the wells with average performance in the field. It can be observed that most of its trajectory is located in interval with good petrophysical properties (the average synthetic porosity of the fractured intervals is 5.7%) and the well IP is considerably higher.



High End Example: WELL-3


The well presented in the picture below is one of the wells with best performance in the field. It can be observed that most of its trajectory is located in the interval with better petrophysical properties in the field (the average synthetic porosity of the fractured intervals is 6.4%) and the well IP is one of the highest.



Blind Test: WELL-X


One of the wells drilled in the most recent pad was completed after this analysis was performed and the results were used as a blind test for the computed correlation (see red point in the picture below).



The actual scope of the work done is greater than what is presented in this post, and In addition to understanding and predicting the initial well production, (IP) a correlation to the Estimated Ultimate Recovery (EUR) was developed.


It was observed that regardless the IP, the wells located in areas with higher Original Gas in-Place (OGIP) presented higher EUR. The picture below illustrates the correlation between the IP, EUR and OGIP.


After predicting the horizontal well IP based on the synthetic porosity and number of fracking stages, the EUR can also be predicted using the chart presented in the picture above and the OGIP map.




Fault Detection (Interpretation) and Impact on Well Productivity


The Proprietary methodology and in-house developed software that allowed to stratigraphically locate the horizontal well trajectory within the reservoir, also allowed to do an interpretation of faulted interval, understanding that this only applies where there are not important lithological changes from the pilot or vertical well used for the correlation with the horizontal (same applies for the horizontal trajectory location within the reservoir and the petrophysical properties extrapolation).


One of the things done to overcome or mitigate this limitation was the integration of the seismic data to validate interpreted faulted intervals.


A very important observation while integrating all the data was that the two wells that were completed in the faulted zone were not in agreement with the computed correlation and presented lower performance than expected.


The fracking stages around the faulted area (high stress) presented very high Initial Shut-in Pressure values (ISIP) during the fracking job. This means that these intervals were more difficult to fracture.


The picture below illustrates on of the wells mentioned above. The interpreted faulted interval was validatedby the seismic data and in the picture it can also be observed that the ISIP values during the fracking job were higher in this interval.


The other well mentioned above, that was completed in a faulted zone, had a Production Log that confirmed that the influx from the fractured intervals around the fault was negligible. The fracking job data also shows that the same interval presented a very high ISIP.



Conclusions


  • In this specific example, Horizontal well performance (IP and EUR) was explained / predicted as a function of effective porosity, number of fracs, and OGIP. It is important to understand that this doesn’t apply to all fields and that the methodology presented in this post only works when there are no dramatic or rapid petrophysical/lithological changes from the vertical/pilot wells to the horizontal legs.


  • Horizontal well placement and geosteering significantly impact productivity. Actively Geosteering horizontal wells helped reducing drilling costs by about 20%, but the critical part of keeping the horizontal wells in the target is the impact that it has on productivity. This could dramatically change the economics for the play.


  • Faulted (High Stress) zones have a negative impact on productivity. They can be identified and intervals around them should be skipped when planning the frac jobs.


  • The work presented in this post can be taken further to defining the optimum number of frac stages in the wells by predicting the IP/EUR and integrating the completion costs.



Bonus Images (Annex)


The picture below shows the correlation derived between average porosity of the fractured interval times the number of fracking stages vs the IP, colored by the average clay content.


Generally speaking the points with higher average clay content correspond to wells with lower IP and lower Porosity*No.Fracs. This might be an important part behind the explanation of the lower performance due to the impact of clay ductility on the fracking jobs.


Finally, the picture below shows the excellent correlation between the average synthetic porosity versus the average IP of all the wells in each pad.



https://www.linkedin.com/pulse/understanding-predicting-horizontal-well-performance-shale-caycedo/


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