ACCURATE HORIZONTAL WELL PLACEMENT THROUGH EVALUATION OF MULTIPLE LWD IMAGES WITH GEOLOGICAL MODELING.

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1 ACCURATE HORIZONTAL WELL PLACEMENT THROUGH EVALUATION OF MULTIPLE LWD IMAGES WITH GEOLOGICAL MODELING. Mark Bacciarelli, Giorgio Nardi; Baker Hughes INTEQ, A. A. Al-Hajari, and S. Ma, Saudi Aramco Copyright 2007, held jointly by the Society of Petrophysicists and Well Log Analysts (SPWLA) and the submitting authors. This paper was prepared for presentation at the SPWLA 1 st India Regional Conference held in Mumbai, India, March 19 20, ABSTRACT Horizontal drilling was the driver behind the development of LWD geosteering sensors and techniques. Early limitations to LWD sensors and bent motor BHAs meant that geosteering techniques provided information for geological correlation and knowledge on the formations the wellbore had penetrated. As LWD propagation resistivity sensors developed and were moved closer to the bit in specialized motor assemblies or integrated rotary steerable assemblies, proactive geosteering techniques that compared actual to modeled data became possible. To aid well placement the sophistication of these models grew to include more complex environmental and geological conditions and full 3-D visualization. As azimuthal LWD measurements become available, quadrant measurements and real time images became an important complement to existing proactive geosteering techniques. These types of tools provided a direction of bed approach and allowed the calculation of structural dip for providing information on the structure being drilled. Today s technology has added high resolution electrical images to a large collection of azimuthal LWD sensors capable of producing a real time images. Collecting data from more than one type of imaging sensors leads to a higher confidence in calculating dip and dip direction. However as targets become thinner and laterals become longer the accuracy of dip picking from images, especially when the well bore has a very low incident angle with the beds (horizontal), becomes problematic. Image or azimuthal information alone will not provide the answers to stay on target. By continuously feeding back images and azimuthal data into the geosteering models which are used by the geologist, and updating these through a quick comparison between different imaging sensors, the earth model can be more accurately re-defined in realtime. This proactive approach of using modeled and image data helps in successful well bore placement and aids in the evaluation of difficult horizontal responses where more than one geological model could be applied. INTRODUCTION The successful placement of horizontal wells, particularly under challenging conditions of dipping structure and thin formations of variable thickness, depends on how the information coming up-hole in real-time can be combined to obtain a model that gets as close as possible to the reality. Before the introduction of real-time borehole images geologists would rely on correlations of log curves and synthetic modeling of the resistivity responses (referred to as 2D modeling) as their prime input into their earth models which were used for well placement. This 2D approach was able to give information on the apparent dip of the formations that had already been drilled. In other words geologists were able to calculate the formation dip that was seen while drilling along a defined direction, not the true dip and dip azimuth of the formation itself (Figure 1). The introduction of LWD tools capable to match each piece of data with the relative position of its sensor by a sub-wall mounted magnetometer achieved the 3 rd dimension in real-time while drilling. This can be considered a milestone in the horizontal drilling technology. Over the past decade real-time borehole images evolved from simple up-down azimuthal measurements 1

2 through four sector/quadrant images, eight and 16 sector images to the high resolution 120 sector images available today. The uses of LWD density and gamma images for geosteering purposes have been described by Greiss et al. 2003, while the development of applications for real-time high resolution LWD electrical borehole images have been described by Lofts et al These different images can now be acquired simultaneously in real-time providing multiple images which complement dip calculations and contrast geological information. Figure 2 illustrates a multiple image acquired in a horizontal well drilled through a clastic sequence. The left most image is a four quadrant gamma image; the centre image is a 16 sector density image and the right track is a high resolution electrical image. Meyer et al (2005) cited typical nominal pixel resolution for a gamma image as 6; a density image as 3.5 and electrical image as In this example bed dip can be determined from all of the images. However the different DOI s (depths of investigation) of the sensors need to be considered to provide more confidence in the dip magnitude calculation. The high resolution clearly shows more valuable geologic detail. The considerations which determine the accuracy of evaluating the dip of geological features (dip picking) from LWD density images have already been recognized by Bornemann et al., Real-time multiple image acquisition poses a problem for petrophysicists. For an objective evaluation program a petrophysicist has to decide whether to use wire-line, LWD, or a combination of the two. If it is decided to use LWD only in the evaluation program it needs to be decided which LWD data needs to be transmitted in real-time (log curves and images). In making these decisions, the petrophysicist needs to consider the combined images and curves which will give, in a particular formation, the best result in terms of real-time utilization for quick operational decisions and after drilling reservoir characterization. The positioning of the imaging tools in the BHA could be critical in obtaining timely real-time data for decision making. Such a multiple imaging program requires the collaborative planning between the 2 petrophysicist, geologist, reservoir and drilling engineer. Real-time borehole image information regarding the true dip and dip azimuth of the geological structure being drilled will provide two unique benefits: 1) Optimization of the well path trajectory. This is particularly important when the geological structure is complicated or when a well is geosteered within a thin reservoir. In these cases high accuracy is required when picking dips, with the occasional need for very precise picks. 2) Revision of the geological model by using more accurate bed dip values to construct more representative synthetic (modeled) resistivity curves. In addition the reconciliation of dips and model resistivity curves can be used to validate some unpredicted tool responses. GEOSTEERING WITH BOREHOLE IMAGES IN THIN RESERVOIRS In cases where an extended-reach well bore has to be positioned in a thin pay zone setting, placing the well path becomes a challenging task. In the following example from a well in Saudi Arabia, the challenge was to keep the well within a 3-4 foot window in a dolomitic carbonate reservoir for 3500 feet of lateral exposure. Under these conditions it takes as little as 350 feet of drilling to traverse the entire pay zone if the well trajectory is misaligned with the formation dip by only 0.5º. Since the reservoir is top and base bounded by a layer of anhydrite, the bit can bounce off the anhydrite causing an undulated well that bounces between the top and bottom boundaries. While the well might remain in the pay zone, this profile has implications on bit life, BHA damage and ultimately the fluid production profile. At the planning stage, the LWD string of tools identified to deliver the most useful information was: 1) Gamma ray, multiple propagation resistivity, azimuthal density and neutron porosity. 2) Density and high resolution electrical images.

3 A BHA diagram of the tools used and their position in the BHA is shown in Figure 3. The geological environment of high resistivity formations was challenging for the high resolution electrical imaging tool (Ritter et al, 2004), which is a conductivity seeking device, so the azimuthal density in real-time would provide a secondary imaging device. Due to expected low gamma contrast it was decided not to transmit a gamma image in real-time. During the actual drilling all information received from downhole in real-time were displayed and compared to the models within the Reservoir Navigation software. Figure 4 shows the modeled geological cross section and displays the real-time density and electrical images with some of the real-time curve data. Since this thin dolomitic carbonate reservoir was bounded by two zero porosity anhydrite layers the conventional geosteering techniques of resistivity modeling, and relying on the look ahead capability of a deep resistivity measurement (400 khz attenuation) and resistivity modeling were inapplicable. The most reliable method was to keep strict control of the azimuthal readings and to have accurate bed dip picks using the high resolution electrical image tool which was positioned as close to the bit as possible. The accuracy of dip picks from real-time borehole images needs to be considered. To calculate the incident angle (a) between the borehole and a given plane in the formation (bedding, fault, etc.) the well known equation is: Where: a = L = D = DOI = L α = tan 1 D. + (2* DO.. I.) Incident Angle Distance in inches between the highest and lowest point in a borehole image sinusoid feature Borehole Diameter (inches) Tool Depth of Investigation (inches) (See Figure 5 for more clarification.) Since the DOI is not a fixed parameter for a given tool, under all conditions, it is important to be aware of the surrounding uncertainty. As a first approach to dip calculation it is possible to estimate the uncertainty of the calculated dip value in relation to the DOI at any given incident angle. To facilitate this task a quick reference graph has been prepared (Figure 6). If we assume that the correct dip (0.0º) of a bed is obtained by a tool that gives an image at 0 DOI we can calculate by trigonometry what it should be if the DOI of the tool is increased. The graph shows these mutual relationships at 7 different incident angles between the tool and the bedding plane. In this example, an increase of DOI from 1 to 2 will cause a variation of the bed dip of about 1.2º when drilling through these layers at a 5º incident angle. It is evident from the equation that the most effected incident inclination is 45º (where we have about 4.5º accuracy range in dip at the same variation of DOI). At the other end of the scale, when the incident angle a gets close to 0.0º (sub-parallel to the image features), and very elongated sinusoids occur, the variation in DOI does not affect the accuracy (0.1º Dip 0.5º incident angle). However in this case azimuth switching can occur requiring a constant evaluation of all the images by the geosteering engineer. For example, Figure 7 shows an outcrop that has greater local variation than the overall dip trend. As long as the geosteering engineer constantly monitors the dips, assuming a structural viewpoint, the overall trend of the dipping formation will be properly observed. Keeping this in mind, we can be confident that, when we utilize images between two boundaries in a narrow pay zone for geosteering, the dip picks will not be noticeably affected by variations in DOI due to any of the potential factors mentioned above. However the level of awareness of DOI effects should be raised when landing the well where the incident angles are higher. In fact, if we underestimate the DOI by 1, we could call a structure horizontal when it is dipping up by about 1.5º. The result would be that at the planned TVD, the landing point would be higher, forcing higher dog-legs in the well path if the target is to be hit. 3

4 Figure 8 shows the dip picks done at x690 ft while landing the well. The well survey at x693 ft was 86.1º inclination and 28º azimuth. The well direction was almost along the strike of the bedding. Different DOI s have been input into the calculation (0, 1, 2 and 3 ). On the four different tadpole tracks the four different calculated dips show no dip variation but a constant rotation of the dip azimuth toward west (from 305.8º to 290.8º) when increasing the DOI from 0 up to 3. These dipping values, obtained from the high resolution electrical image, can be entered in the Reservoir Navigation software to be crosschecked with the real time data and the modeled data. In Figure 9 two cases are displayed (B and D) corresponding to 1 and 3 DOI respectively. In the section track the well-path trajectory (red) is plotted against the modeled section of the reservoir. Both figures 8 and 9 utilize a semi dynamic colour mode which is a non linear colourization scheme designed to enhance bed features for dip picking. In Case B, the apparent dip along the bore-hole direction shows that the well had been drilled down dip. If this were true, the anhydrite below the tool could not have been entered. The last two tracks show the correlation between the real-time (red) and the modeled (black) bulk density and gamma ray. With a 3 DOI for the electrical borehole imaging tool the modeled curves fit the actual ones. Since the high resolution electrical imaging tool is the closest imaging tool to the bit (see Figure 3) thus it plays a major role in the geosteering operation. It is a good practice to benchmark its DOI with the model during the landing of the well not just for the horizontal section when the well-bore has been landed inside the reservoir. Dip picks from the high resolution electrical imager should be cross checked with picks from the density imager when the incident angle is above 4º. RESISTIVITY RESPONSE ANALYSIS UTILIZING IMAGE DIP PICKED VALUES (BACK-WARD MODELING) During the 8 ½ hole section drilling of a horizontal well in a shaly sand reservoir in Saudi Arabia the real-time resistivity curves from an LWD propagation resistivity tool showed some response features that required further investigation. In particular it is noticed that the 4 separations of resistivity measurements from 7800 ft MD to 9000 ft MD where the two phase shift curves showed spikes and out of range along several intervals. The well targeted a main sand body, which consists of inter-bedded deltaic sands, shales and silts. It could be surmised that crossing such layers at a low incident angle could cause such a propagation resistivity response, but the data is far from conclusive and tool malfunction could be diagnosed leading to an unnecessary trip for failure. Additionally the resistivity values do not provide useable inputs for saturation calculations. Bed dip analysis performed on the azimuthal density image log identified the presence of a dense shaly layer that intersects the well-path at different points. From this data, it was possible to create a dipping bed model that showed an undulated structure along the well-path. A true resistivity squared profile was created from data from the landing phase and used as the input into the resistivity model. This model calculates the resistivity response of any given LWD tool along a given well-path and structure. This approach is important when dealing with responses from horizontal wells, where the resistivity values could be very different from a vertical well log recorded in the same formations. It should be acknowledged that the model will always be a simplified version of the reality. When the modeled responses deviate from the actual responses an iteration process is used where the initial R t profile and the structure dip values are adjusted to provide a match. Figure 10 shows the final response model on a TSD (True Stratigraphic Depth) scale for this example. The black squared curve is the Rt curve. The presence of a 0.6 foot thick shale layer is evident by the drop in resistivity down to 4 ohmm. A particular feature of propagation resistivity logs in horizontal wells is the development of spikes, so called polarization horns when the well-path crosses two formations with a high resistivity contrast and at a low incident angle. This effect is more accentuated for the phase shift measurement and higher frequency measurements. For this reason the 2 MHz phase resistivity (RPCHM) is expected to be the most affected reading, followed by 400 khz phase resistivity (RPCLM) and then the 400 khz attenuation resistivity

5 (RACLM). The model shows that the development of polarization horns for the given input conditions are expected in a 3 foot TSD range around the shale layer. In Figure 11 the same model of propagation resistivity is plotted on a Measured Depth (MD) scale, together with the evaluated dipping structure and the actual resistivity and azimuthal density log. It is evident that the real response of the log has been validated by the model and shows where polarization horns and out of range values would be expected to occur. This matched the actual responses. The up/down density curves confirm the validity of the structure dipping model. The validation of the model provides a useable Rt for petrophysical calculations. From the log itself, resistivity values could not be input into a petrophysical model, however probable resistivity values could be derived from the resistivity profile input in the model. CONCLUSIONS The introduction of different types of LWD borehole images, in conjunction with conventional logging data, has been very beneficial in positioning horizontal wells within the targeted pay zones. This increased information carries the necessity of understanding the benefits of reconciliation of real-time images and curves with modeled data. Before this was available it was common practice to correlate only the real time logged curves with the modeled curves. Adding borehole images to the geosteering models has enhanced interaction in geosteering and ultimately leads to more successful wells drilled. In this paper two different examples of data reconciliation have been presented: Real-time and modeled curves utilized to evaluate the most accurate depth of investigation of different borehole images for more accurate evaluation of the structural dips in thin reservoirs. Bedding dips picked from an LWD image have been entered in a geosteering model to fine tune the R t and explain the real-time resistivity curves. 5 In the first case it was shown how, at particular incident angles, the DOI of an image tool could lead to different bedding scenarios if its input value was changed in the geosteering software. In the second example a problematic log showing spikes and curves out of range could be replicated with the modeling software (with more accurate structural dip input from values derived from an LWD density tool) providing useable data. This has important implications in using modeled logs, once validated, in petrophysics. To get the best value of running multiple LWD images a well thought out pre-job plan is critical to decide which images would be beneficial as well as their position in the BHA. ACKNOWLEDGMENTS The authors wish to thank Saudi Aramco and Baker Inteq management for their support. The following are acknowledged for their help during this study, Saudi Aramco geosteering operation center and the following individuals: Gavin Lindsay; Jeremy Lofts; Stephen Morris; Ismail Ozkaya; Terry Quinn; Raymond Chew; Ravan Ravanov. REFERENCES Bornemann E., Bourgeois T., Bramlett K., Hodenfield K., Maggs D., The application and accuracy of geological information from a logging-while-drilling density tool, SPWLA 39 th Annual Logging Symposium, Keystone, USA. Greiss R-M., Webb C.J., White J., McDonald B., Flanagan K., Rodriguez J., Scholey H., Real Time density and gamma images acquired while drilling help to position horizontal wells in a structurally complex North Sea field. SPWLA 44 th Annual Logging Symposium, Galveston, Texas, USA. Lofts J.C., Morris S., Ritter R.N., Chemali R., Fulda C., High quality electrical borehole images while drilling provides faster geologicalpetrophysical interpretation, with increased confidence. SPWLA 46 th Annual Logging Symposium, New Orleans, The USA.

6 Meyer N., Holehouse S., Kirkwood A., Zurcher D., Chemali R., Lofts J., Page G., Improved LWD density images and their handling for thin bed definition and for hole shape visualization. SPWLA 46 th Annual Logging Symposium, New Orleans, The USA. Ritter R.N., Chemali R., Lofts J.C., Gorek M., Fulda C., Morris S., Krueger V., High resolution visualization of near wellbore geology using while-drilling electrical images, SPWLA 45 th Annual Logging Symposium, Noordwijk, The Netherlands. ABOUT THE AUTHORS several papers in Petrophysics and well monitoring logging. He has 20 years experience dealing with sand shale and carbonate reservoirs. Shouxiang Mark Ma is a PE Specialist and a mentor in the Technologist Development Program at the PE organization, Saudi Aramco, Mark received a PhD degree in PE and has published more than 30 papers in log/core petrophysics. Before joining Aramco, he worked 20 years in the industry and academia including PRRC, WRI, and EPR. shouxiang.ma@aramco.com. Mark Bacciarelli received his B.Sc in Petroleum Geology from Imperial College, London in He began his career as a geophysicist before moving to the oilfield. He has accumulated over 25 years experience in oilfield operations in the varied fields of Surface Logging, Logging While Drilling, Software Applications Support, Directional Drilling and Geosteering. Since 1999 he has held the position of Regional Petrophysicist responsible for LWD petrophysical and geoscience support and development for Baker Hughes INTEQ in the Middle East and Asia Pacific Region. Giorgio Nardi graduated from the University of Trieste (Italy) in Marine Geology in 1988 and joined Baker Hughes in Since then he spent 12 years on international field assignments as a Surface Logging Geologist, Logging While Drilling Engineer and Reservoir Navigation Engineer. Since 2002 he has been leading the regional team of Reservoir Navigation Engineers for Baker Hughes INTEQ in the Middle East and Asia Pacific Region. His current position is Business Development Manager for Reservoir Navigation responsible for geological well placement, geosteering related modeling and reservoir navigation applications development. Abdalrasool A. Al-Hajari is a supervisor in Reservoir Description Division of Saudi Aramco. He has BS degree in petroleum engineering from King Fahed University of petroleum and minerals. He has worked in different areas of petroleum engineering, drilling, production and reservoir management. He has co-authored 6

7 FIGURES Figure 1. A 2D model example showing a comparison between actual and modeled/offset log curves while landing a well in the reservoir. The bedding is flat (apparent dip) but in reality this well has been drilled along strike, with 4º dip, orthogonal to the well direction. 7

8 Depth m x x x Figure 2. Quality comparison of 3 different images. From the left: Gamma Ray, Density and Resistivity 8

9 Figure 3. Diagram of the Bottom Hole Assembly utilized in well A in Saudi Arabia with the sensor offsets: 6 5 1) Near-Bit Inclinometer, 3.9 ft 2) High Resolution Electrical Image, 19.8 ft 3) Equivalent Circulating Density, 25.7 ft 4) Gamma Ray (Azimuthal), 25.6 ft 5) Multiple Propagation Resistivity, 30.6 ft 6) Directional, 36.4 ft 7) Azimuthal Density, 62.8 ft 8) Acoustic Caliper, 65.1 ft 9) Neutron Porosity, 72.9 ft

10 Figure 4. Example of Reservoir Navigation Active Screen Tracks 1 and 2 show the correlation between the actual density/neutron curves (red & blue) with the modeled ones (black). Third track is up and down azimuthal densities, which have been shaded so red shows higher up density and blue higher down density. Next are resistivity curves in tracks 5 and 6 (actual and modeled). It is quite evident that little information of value is provided by these flat curves. There are 2 reasons for this: the high resistivity environment (dolomite/anhydrite) and the averaging of non-azimuthal readings in thin layered structures. Tracks 6 and 7 show density and high resolution electrical Images. In this case there are significant contrasts in density values between dolomite and anhydrite of about 0.35 g/cc so the density image provides valuable information but is 62 ft behind the bit. The electrical image has enhanced resolution and, as it was acquired just 19 ft from the bit, was the main tool to steer the well path in the pay zone. Track 8 is called the Image Predictor. It shows the theoretical sinusoids expected from the interaction between the actual well bore and the modeled structural dip. Track 9 is a cross section with the actual well path plotted (red). In addition actual (green) and modeled (black) gamma ray curves are plotted. 10 D

11 Figure 5. The relationship of incident angle, DOI and bed dip with a borehole image. L = Distance in inches between the highest and lowest point in a borehole image sinusoid feature. D = Diameter of borehole Image Calculated Dip Variation with increasing D.O.I. 0-1 Image Calculated Dip (deg.) Incident Angles 10º 5º 4º 3º 2º 1º 0.5º " 0.25" 0.5" 0.75" 1" 1.25" 1.5" 1.75" 2" 2.25" 2.5" 2.75" 3" Depth of Investigation (inches) Figure 6. This graph helps identifying the degree of confidence in the dip pick values in relation with a potential error quantifying the DOI of a given tool. The incident angles shown here are between 10º and 0.5º. Only the variation of dip is considered here, with no shift in dip azimuth. In this example; at 5º incident angle a DOI difference of 1 will give a difference of just over 1º of dip. 11

12 Figure 7. Outcrop showing greater local variations than the overall dip trend. Figure 8. High resolution electrical image displayed in semi-dynamic colour mode. The sinusoid has been picked once and then different DOI s have been applied to calculate the dip and the dip azimuth. Well bore survey at x,693 ft = 86.1º inclination, 28º azimuth. 12

13 Figure 9. The dip picked from the high resolution electrical image in Case B and Case D of Figure 8 are applied to the Reservoir Navigation modeling software. The same sinusoid is also plotted against the density image for a cross-check. The bulk density and gamma ray curves are compared with the modeled curves derived from offset well data. Case D shows a good match. The DOI of the electrical image in this reservoir and at these drilling conditions could be set confidently at 3. 13

14 Figure 10. TSD (True Stratigraphic Depth) Resistivity Model. The black RTM (Resistivity True Modeled) curve is obtained when the modeled curves show the best match with the actual ones under the bed dipping conditions obtained by the density image. 14

15 A B Figure 11. A) Density image interpretation for bedding and fractures. B) MD (Measured Depth) Resistivity model. Bedding values imported in the Reservoir Navigation software. It shows the validation of the actual logged curve with the response curves expected by the tool under given conditions of R t and dipping bed values. 15