Using pre-drill seismic and LWD data for safe, efficient drilling

Predicted earth stress is checked as the well is drilled to determine necessary changes in drilling plans, thus allowing even bad and ugly wells to be carried to completion

Dr. Ben A. Eaton,  President, Earth Stress Prediction Center, Houston

arth stress predictions are calculations made to estimate the magnitudes of the stress gradients known as overburden, pore pressure and fracture pressure. Such estimations are absolutely required as the framework for developing good well plans and for conducting safe and efficient drilling operations. Data used in the prediction equations includes the following:

• Formation bulk densities
• Formation interval velocities
• Formation sonic travel times
• Formation wireline log resistivities
• Formation LWD resistivities
• Formation LWD sonic travel times
• Formation velocities from VSP and check shot data
• Formation properties from actual offset and currently drilling wells
• Formation pore pressures and fracture pressures from well tests, kicks and leak-off test data.

For more than thirty years such data have been collected and now comprise what is known as the Earth Stress Prediction Center Databank. These data have been utilized over the years to develop the equations and computer model currently in use to do the following:

• Prepare the earth stress prediction profiles versus true vertical depth, which form the basis for good drilling well plans.
• Analyze the data to predict the stress levels into which the anticipated reservoirs are likely to fall. Then, the expected reservoirs are classified as to their probability of being good, bad or ugly.
• Monitor real time drilling activity via a modern communication system from the offices of the Earth Stress Prediction Center. Here, the predictions may be modified rapidly.

Reservoir Types

First the reader must be fully aware of the definitions of good, bad or ugly reservoir conditions. These definitions are based on economics and risks. The following is the basis for these definitions:

Good reservoirs have very good producibility qualities, great profit potential and very low drilling risk. These reservoirs occur or exist above the top of geopressure and down into the geopressure zone. Usually, they are found no deeper than 2,000 to 3,000 ft below the top of geopressure.

Bad reservoirs still have profit potential, but involve much greater costs and risks to drill and complete. These reservoirs usually exist in the window between 1,000 to 6,000 ft below the top of geopressure. They do produce with sufficient qualities, allowing up-to-great profit potential.

Ugly reservoirs are those that may or may not contain hydrocarbons, and exist in such an elevated earth stress state that profitable or economic production is very unlikely. The reservoir matrix stress ratio is extremely low while, at the same time, the respective pore fluid stress ratio is extremely high, as related to total overburden stress. These reservoirs usually exist at depths of 5,000 ft or more below the top of geopressure. Also, they usually are quite hot. The main thing is that they do not produce profits, and drilling and completion costs and risks are very high. In summary, reservoirs may be defined as in Table 1.

In many cases, drilling a deep well is a very risky and expensive undertaking. This is especially true for remote locations in deep water, or other sparsely drilled areas. At times, only the original seismic data exist due to the fact that no offset wells have been drilled in the area.

In the above situations, geophysicists can be of real service to the drillers. Seismic data for several shot points in the immediate vicinity of a proposed well must be processed so that the geophysicists can pick true vertical depth of intervals and their respective average interval velocities. The greater the number of intervals picked, the better. In other words, depths and average interval velocities should be picked as carefully as possible, producing thin intervals for a well of a given proposed depth. Thick intervals are less desirable, while numerous thin intervals are best for prediction work.

Actual Well Examples

Example A — West Africa, offshore. The reason one must have as many intervals as possible is that these velocities are converted to travel times, and a synthetic sonic log is produced for prediction work. Such synthetic sonic values are plotted as shown in Fig. 1, which is just a typical, real example of such a data plot. These data are from an area of medium water depth, offshore West Africa. Note that Fig. 1 shows data for many intervals that are fairly thin. Fig. 2 represents a stress gradient profile resulting from the data in Fig. 1 and the calculation methods developed by Eaton.1 From left to right, the stress gradients of pore pressure, fracture pressure and overburden are shown versus true vertical depth, Fig. 2. This is the basic well plan graph the operator used to plan the well. A mud weight program was selected to provide a small amount of overbalance at all depths.

While drilling well A, the operator transmitted resistivity data, obtained with LWD tools, to the Earth Stress Prediction Center. These data were plotted at regular time intervals, and the result is shown in Fig. 3. Fig. 4 shows how the actual drilling resistivity data compared to the pre-drill seismic-predicted values of pore pressure stress gradients. Also, note the great agreement between the seismic data fracture stress gradient curve and the actual LOT values. Well A was drilled very safely and efficiently. There was no lost time due to kicks, blowouts, etc.

Example B — South Louisiana, offshore. Pre-drill seismic data travel times from a single shot point are shown in Fig. 5. Again, the intervals picked are numerous and thin. These data were used with data from the databank to produce the earth stress profile (Fig. 6) for this location. Extreme stress levels can be observed. These stress levels lead to ugly type reservoirs, as shown in Fig. 7.

The well was drilled to below 18,000 ft TVD and logged. Fig. 8 is a plot of wireline log conductivity values. Fig. 9 represents an earth stress profile based on log data of Fig. 8. Note the various values of LOTs (shown in Fig. 6) before and after squeeze cementing each casing shoe. The fracture stress gradient calculation is excellent. Fig. 10 represents a comparison of shot point seismic velocity-predicted pore pressure (pre-drill), conductivity log-derived pore pressure (post-drill), and the actual mud weight used to drill the well safely and efficiently (post-drill). It is obvious that the pre-drill prediction was quite good, and the stress levels for ugly reservoirs did exist.

Example C — South Louisiana, onshore. In this example, pre-drill velocity data (converted to Dt) were plotted as shown in Fig. 11. The earth stress profile is shown by Fig. 12 for well C. In this case, the data had been averaged and smoothed, which gives an unreal appearance on the charts. The overall magnitudes are fairly good. Extreme stress levels were predicted, as shown in Fig. 12, and the resultingugly condition was predicted, as shown in Fig. 13. Fig. 14 shows post-drill results. Again, the well was drilled safely and efficiently, but the result was ugly, as predicted.

Example D — Deepwater, Gulf of Mexico. In this case study, only a single shot point velocity and depth data set was provided for prediction work. Fig. 15 shows the synthetic sonic data plot from this single shot point. The resulting predicted earth stress profile is shown in Fig. 16. Note that the pore pressure, fracture pressure and overburden curves approach each other at depth in this case. The resulting predicted reservoir classification is shown in Fig. 17.

The post-drill analysis for this deepwater GOM well is shown in Fig. 18 and Fig. 19. Fig. 18 shows the predicted stress profile, as well as actual mud weights used to drill the well. Note the close agreement. Also note the excellent LOT and predicted fracture gradient agreement. Fig. 18 is a comparison of LWD data and the pre-drill prediction. Also shown is the post-drill log analysis.

Conclusions

It is possible to predict the earth stress profile for a given drilling location, before drilling the well, using shot point data. Tracking the well, using LWD data and modern communication systems, can be done from the databank center. In this manner, the predicted stress profile is checked using LWD data as the well is drilled. If the data center detects necessary changes in the well plan, which has been based on the pre-drill prediction, then these changes are transmitted to the rig and drilling offices. Thus, safe and efficient drilling operations are carried to completion.

Literature Cited

¹Eaton, B. A., and T. L. Eaton, "Fracture gradient prediction for the new generation," World Oil, October 1997.

The author

Dr. Ben A. Eaton is President of Earth Stress Prediction Center, which he founded in February 1998 as a division of Eaton Industries of Houston, Inc. He earned BS (1962) and PhD (1965) degrees in petroleum engineering from Marietta College and the University of Texas at Austin, respectively. Following graduation, he worked for Conoco in production/drilling engineering; for Gulf Oil in completion/stimulation; and for Union Oil Co. of California in reservoir/secondary recovery. In 1970, he founded Universal Drilling and Engineering Consultants, a Texas-United subsidiary. Eaton Industries of Houston was founded in 1974. In 1977, the firm, Hydrocarbon Exploration Co. was added to incorporate oil/gas exploration activity. Another successful business venture was the formation, and later sale of, Eaton Publishing Co. Mr. Eaton has been active in teaching several drilling/completion courses. He is a registered professional engineer in Texas and a member of SPE, Pi Epsilon Tau, Tau Beta Pi, and Sigma Xi. He has authored/co-authored 19 articles/papers for SPE and World Oil.

Copyright © 1999 World Oil
Copyright © 1999 Gulf Publishing Company