DDAAYY 22 SSEESSSSIIOONN 88 GGEEOOLLOOGGYY PPAAPPEERR 1111

March 2010 133 

SUCCESSFUL APPLICATION OF REAL-TIME PORE PRESSURE AND FRACTURE GRADIENT MODELING IN

DEEPWATER EXPLORATION WELLS

Don S. Basuki Baker Hughes

The accurate modeling of pre-drill, while-drilling and post-drill pore pressure and fracture gradients

(PPFG) for an exploration well is a very challenging process, particularly in deep water applications. Erroneous PPFG predictions and estimations can be a source for unexpected non-productive time in drilling operations due to wellbore integrity or pore pressure related problems resulting in significant cost overruns for the well. In addition to preventing geopressure related problems while drilling, other benefits of real-time PPFG modeling include identification of formation breathing, better hole cleaning, higher rates of penetration and prevention of differential sticking.

This paper presents two case histories of successful PPFG modeling for deep water exploration wells in North Africa and Southeast Asia. The pre-drill PPFG models were prepared using sparse offset well data. In the drilling phases, the models were updated and calibrated based on mud weight, caving shape and leak-off test information. Eaton’s resistivity PPFG method was used to estimate the PPFG model in real-time. In addition to Eaton’s resistivity method, Matthews’ and Kelly’s method was also used in predicting the fracture gradient. A hydraulics model was run in parallel to the drilling operation, so that real-time ECD (equivalent circulating density) could be compared against theoretical values to detect anomalies and to ensure that the annulus pressure stayed within the predicted PPFG window. At the end of the drilling phase, the real-time PPFG model was found to be within 0.1 ppg of actual formation testing pressure readings.

Based on the two case histories, the paper will illustrate in detail the process steps required for real-time PPFG modeling and demonstrate the benefits of taking the proper actions to mitigate risks identified by the PPFG model. INTRODUCTION

The oil and gas industry suffers in particular in deep water exploration well drilling from costly non-productive time (NPT) events due to geopressure problems. Some studies suggest these costs to exceed several billion US$ on a global basis per year. The geopressure related problems include but are not limited to kicks, loss circulation, stuck pipe and lost-in-hole events, sometimes forcing the operator to re-drill a well section or even an entire well. Abnormally high formation pressures or over-pressured zones occur worldwide in all sedimentary basins. An additional complication in deep water is that the formation strength (or fracture gradient) is lower than in land or shallow water. As a result, the window between pore pressure and fracture gradient is relatively narrow, leaving little margin for drilling related pressure changes such as swab/surge effects or ECD increases.

Maintaining an accurate pore pressure and fracture gradient (PPFG) model while drilling is one way to minimize the risk of geopressure related hole problems. The quality of pre-drill PPFG models relies on the availability of offset well data and geological information of the proposed well, including quality seismic data. The less data are available from offset wells, the more uncertainties are in the pre-drill model and the less reliable it will be. However, more information and data become available as the well is drilled, for example leak off test/formation integrity test, MWD/LWD data (gamma ray, resistivity, acoustic, images and formation tests), connection gas, caving size and shape of the cavings; all valuable information to update and calibrate the pre-drill PPFG model in parallel with the drilling process. With correct PPFG models, loss zones and pressure ramps can be identified and anticipated in forward modeling, so that the rig can prepare for proper risk mitigation actions and execute them with minimum time losses if necessary. Finally, in post-drilling the PPFG models can be tied in with wireline formation testing data if available.

METHODOLOGY

The US Drilling Engineering Association (DEA) project 1191 classifies methods to predict pore pressure into two general approaches: (1) direct methods, and (2) effective stress methods. The effective stress

Petroleum Geology Conference and Exhibition 2010 29-30th March, 2010 Kuala Lumpur Convention Center, Kuala Lumpur, Malaysia      GGEEOOLLOOGGYY PPAAPPEERR 1111

134 Geological Society of Malaysia 

methods are classified into vertical, horizontal and other methods. In total there are 15 methods to predict pore pressure according to DEA Project 119 classification. The methods applied while drilling for the two projects described in this paper are Eaton’s resistivity and Eaton’s Dxc (corrected drilling exponent). In the post-drilling phase after receipt of acoustic data from wireline logging, the Eaton’s method based on acoustic data was applied. All three methods are described in detail by Eaton. 2

For estimating the fracture gradient, the DEA projects classifies into four categories, depending on the consideration of fracture creation: (1) minimum stress methods (losses if long cracks developed against the minimum principle in-situ stress), (2) hoop stress methods (losses if short cracks are created at the borehole wall), (3) fracture mechanics methods (based on the theory of fracture propagation for all length cracks), and (4) direct methods (no underlying theoretical model). Based on these classifications, there are 25 methods to predict the fracture gradient.3 In addition to Eaton’s fracture gradient method4, in the projects described below also the Matthews’ and Kelly’s5 method was used.

The overburden gradient (OBG) is defined as the stress created by the weight of the materials above the depth of interest divided by the vertical depth (lithostatic pressure). The prediction of the overburden gradient is not straightforward and typically has to be approximated in an exploration well scenario, since the density of the overlaying sediments is still unknown in the pre-drill phase. Traugott’s empirical relation method6 – also known as Amoco’s method – was applied to calculate the overburden gradient for the two projects below.

Important calibration information such as Leak-Off Test (LOT) and Formation Integrity Test (FIT) results, occurrence of cavings including size and shape, connection gas volumes, as well as torque and drag trends were also taken into consideration to verify and update the PPFG model in real-time. In addition, a hydraulics model was prepared and updated in parallel to the drilling operation to compare predicted results with real-time ESD (equivalent static density) and ECD (equivalent circulating density) derived from downhole annulus pressure measurements. The comparison of theoretical vs. actual numbers helps to identify anomalies in the drilling process at an early stage.

Real-time PPFG service is not a stand-alone service, rather it gathers all data and information from various sources. The engineers who perform the service have to take all available data into account to identify what is going on down hole from the geopressure-related perspective. There are two approaches to perform PPFG model updates in real-time: (1) at the rig site, and (2) from remote location with real-time data streaming from rig site. Some disadvantages of being remotely located to perform the PPFG model update are: it relies on data transmission quality from the rig site, and the inability to directly check physical evidence of drill cuttings and cavings for indications of overpressure. For remotely located services someone knowledgeable and competent to describe physical conditions of drill cuttings and cavings is needed at the rig site. High quality digital cameras directed on the shaker tables might improve the situation in the long run, however, they also need to be supported by sufficient data bandwidth to shore.

In both projects, two dedicated engineers were on location to perform the service in 24 hours coverage while drilling. They gathered various data and information mainly from mudloggers, MWD/LWD engineers, mud engineers, directional drillers, drillers, and wellsite geologists. The engineers made real-time models from these data and compared them with pre-drill model. The updated PPFG models were reported on a regular basis, typically as contribution to the morning meeting. If elevated risks of geopressure related problems were detected from physical evidence and an updated PPFG model in between, the dedicated engineers would notify the operator representatives immediately with recommended actions to mitigate the identified risks.

CASE HISTORY #1

This near vertical exploration well (Fig. 1) was drilled offshore North Africa in a water depth of slightly more than 2000 meters (6562 feet). The main cause for overpressure formations in this area is tectonic stress with compaction disequilibrium as secondary cause. In pre-drill PPFG modeling, a pressure ramp was prognosed at a certain depth, however, was not detected while drilling. As a result, the planned mud weight increase was not necessary at this point. Without the real-time PPFG modeling, the mud weight would have been increased as per program, which would have resulted in elevated risk of differential sticking and probably reduced rate of penetration (ROP) due to high overbalance.

DDAAYY 22 SSEESSSSIIOONN 88 GGEEOOLLOOGGYY PPAAPPEERR 1111

March 2010 135 

One of the objectives in this well was to maintain bottom hole pressure within 100 psi from the actual pore pressure while drilling. Based on the real-time PPFG model the mud weight was adjusted on a regular basis. All objectives of the well were successfully achieved. Hole cleaning was not an issue since this well was near vertical and mud flow was enough to clean the hole. Compared with the actual pressure data from a wireline pressure tester, the pore pressure model established while drilling was found to be accurate within 0.1 ppg difference.

CASE HISTORY #2

This vertical exploration well (Fig. 2) was drilled in offshore Southeast Asia, in a water depth of over 4000 feet (1219 meters). The main cause for overpressure formations for this area is compaction disequilibrium with clay diagenesis as secondary cause. This project only used Eaton’s resistivity method for pore pressure estimation in both pre-drill and real-time models. Fracture Gradient estimations were based on Eaton’s method and Matthews’ and Kelly’s methods. Several pressure ramps were identified in the pre-drilling phase. These ramps were present in the drilling phase, though at slightly different depths compared to prediction. As the model was adjusted in real-time for these depth-differences, the mud weights were successfully adjusted. The bottom hole pressure while drilling was successfully maintained less than 300 psi above actual pore pressure while drilling.

The pressure ramp at point A in the model in Figure 2 was confirmed in the drilling phase by the detection of concave and splintery cavings (Fig. 3 and 4) at the shaker tables on the rig site. These types of cavings are created by tensile failure along the wellbore, which usually occurs when the pore pressure in shale exceeds the hydrostatic pressure of the drilling fluid column. The mud weight was planned to be gradually increased; however, due to a sudden drop in ECD and a sharp increase of the pore pressure in the real-time updated PPFG model, the mud weight was increased immediately. From post-drill wireline pressure test results, the presence of the pressure ramp was confirmed. It can be concluded that the timely reaction to the predicted pore pressure increase was successful in avoiding a potentially costly NPT event including well control measures. After finishing the well, the PPFG model updated in real-time was found to be within 0.1 to 0.2 ppg of the actual pressure readings taken by a wireline pressure testing tool.

CONCLUSIONS

• Drilling  exploration  wells  in  deep  water  is  very  challenging  with  many  unknown  parameters, including risk from geopressure related challenges. Developing an accurate PPFG model in the pre-drill phase is a key requirement for minimizing these geopressure related risks. However, with limited offset well information available in exploration drilling, the model has a high degree of uncertainty and a lot of assumptions must be made.

• Monitoring and updating the pre‐drill PPFG model  in the drilling phase can reduce this uncertainty significantly. Utilizing  the  proper  LWD  tools  in  real‐time,  such  as  resistivity,  acoustic,  images  and formation  tests  for a  real‐time, proactive PPFG and wellbore stability analysis can provide a fully calibrated model which fully informed decisions can be made on optimizing the drilling fluids program thereby reducing the non-productive time associated from geopressure.

• Two case histories of exploration are presented in which dedicated resources were put in charge of updating the PPFG model. In the first case, no NPT due to geopressure events were encountered. In the second case, a potential NPT due to geopressure problem was successfully avoided based on accurate PPFG real-time monitoring and immediate reaction to mitigate the risk.

NOMENCLATURE DEA = Drilling Engineering Association Dxc = Corrected Drilling Exponent ECD = Equivalent Circulating Density ESD = Equivalent Static Density FG = Fracture Gradient FIT = Formation Integrity Test LOT = Leak-off Test

LWD = Logging While Drilling MW = Mud Weight MWD = Measurements While Drilling NPT = Non-Productive Time OBG = Overburden Gradient PP = Pore Pressure PPFG = Pore Pressure and Fracture Gradient

Petroleum Geology Conference and Exhibition 2010 29-30th March, 2010 Kuala Lumpur Convention Center, Kuala Lumpur, Malaysia      GGEEOOLLOOGGYY PPAAPPEERR 1111

136 Geological Society of Malaysia 

ACKNOWLEDGEMENT

The Author wishes to thank Dr. Ing. Gerald Heisig (Baker Hughes, Kuala Lumpur), Dr. Stefan Wessling (Baker Hughes, Celle), Michael Reese (Baker Hughes, Lafayette) and Christopher Wolfe (Baker Hughes, Houston) for reviewing this paper and Baker Hughes for permission to publish this paper.

REFERENCES Bowers, G., 1999, “State of the Art in Pore Pressure Estimation”, DEA Project 119 Report No. 1. Eaton, B. A., “The Equation for Geopressure Prediction from Well Logs”. SPE 5544, 1975. Bowers, G., 1999, “State of the Art in Fracture Gradient Estimation”, DEA Project 119 Report No. 3. Eaton, B. A., “Fracture Gradient Prediction and Its Application in Oilfield Operations”, Journal of Petroleum

Technology, Oct., 1969, pp. 1353-1360. Matthews, W. R., and Kelly, J., “How to Predict Formation Pressure and Fracture Gradient”, The Oil and Gas

Journal, Feb. 20, 1967, pp. 92-106. Traugott, M. O., 1997, “Pore/Fracture Pressure Determinations in Deep Water”, World Oil – Deepwater

Technology Supplement, August 1997, pp. 68-70.

Figure 1 – Pore Pressure and Fracture Gradient Model from Well #1, Offshore North Africa.

Figure 2 – Pore Pressure and Fracture Gradient Model from Well #2, Offshore Southeast Asia.

Figure 3 – Concave caving from Well #2, side view. Figure 4 – Splintery caving from Well #2, top view..