Using Horizontal Well Drilling Data To Predict Key Rock Properties For Unconventional Wells In Canada And Optimize Hydraulic Fracturing Design September 17, 2014 Prasad Kerkar, Production Technologist, Shell Int’l E&P Inc. Session: Horizontal Completions from the Drillers Perspective 3 rd Annual Horizontal Drilling Canada Version 11-09-14 1
Acknowledgments Hareland, Geir, Harcon Inc. Williams, Deryl, Innovate Calgary Fonseca, Ernesto, Shell International E&P Inc. Hackbarth, Claudia, Shell International E&P Inc. Mondal, Somnath, Shell International E&P Inc. Bell, Sarah, Shell Canada Ltd. Azad, Ali, Shell Canada Ltd. Savitski, Alexei, Shell International E&P Inc. Wong, Sau-Wai, Shell International E&P Inc. Dykstra, Mark W, Shell International E&P Inc. Dudley, John W, Shell International E&P Inc. Dixit, Tanu, Shell Canada Ltd. Eggenkamp, Irma, , Shell Canada Ltd. Parker, Jerre L, Shell Global Solutions US Inc. 2
Key Message ! Routinely acquired drilling data can compute formation un/confined compressive strength and Young’s modulus. ! This presentation shows motivation behind the workflow and its application to understand lateral heterogeneity in Groundbirch Montney lobes. ! Workflow performs wellbore friction analysis to estimate ! Workflow performs wellbore friction analysis to estimate downhole weight-on-bit and couples it with ROP models developed for PDC/Rollercone bits. ! Young’s modulus/UCS signatures can be used in correlation with fracture gradient to engineer placement of perforation clusters along the lateral in the hydraulic stimulation design. 3
Technology Enablers Challenges Business Impact Solution ! Layers of rock with variable ! Estimation of rock strength ! Better well planning strength and toughness using drilling data could ! Better completion avail UCS and YM logs on ! No direct estimation of Rock design every well drilled Young’s modulus which ! Rock strength logs controls fracture growth ! Depth- and time- based could be available on drilling data is acquired ! Wirline logs are acquired on every well drilled from Exploration on every well a few wells exploration to ! Results can be calculated production. ! Log require rig time and in real time significant processing ! Saves waiting on post- ! Saves waiting on post- ! Extrapolation from sonic logs ! Extrapolation from sonic logs drilling wireline logging across plays introduces uncertainty 1 Development FRACTURE PREDICTION WELL DESIGN UCS, YM LOGS SEISMIC EVALUATION REAL TIME OPTIMIZATION 4 1. Figure adapted from: Eshkalak, M.O.et al., Paper SPE 163690-MS, 2013 as an example of synthetic geomechanical logs.
Methodology (1/3) 2. Wellbore friction 1. Sheave HL, HL-wt 3. Downhole Weight coefficient (µ), of hook, HL after SPP on Bit (DWOB) Calculated HL α − α α − α sin sin cos cos top bottom top bottom β ∆ α − × β ∆ α F = w L cos or µ w L sin or − n top HL 1 e α − α α − α lines obs ↓ top bottom top bottom SheaveHL = . ...( ) − ( ) n 1 e − θ µ − [ − ] × lines + F DWOB or F DWOB e ...( no bending ) bottom bottom − 1 − e e . . 1 1 α − α α − α sin sin cos cos n HL e lines top bottom top bottom β ∆ α − × β ∆ α obs ↑ F = w L cos or µ w L sin or SheaveHL = . ...( ) top α − α α − α − n e 1 top bottom top bottom lines ( ) − θ µ × + F or F e ...( bending ) bottom bottom e = individual sheave efficiency F top = tension on the top of each w = unit pipe weight n lines = no. of lines between blocks drill string element = length of each drill string ΔL F bottom = tension on bottom of each = when lowering the blocks ↓ = inclination angle α drill string = when raising the blocks ↑ µ = wellbore friction coefficient = buoyancy factor β 5
Methodology (2/3) 4. Sliding correction to DWOB, Relative 5. ROP Models for a 6. ROP Model for a abrasiveness PDC drill bit Rollercone drill bit constant calculation b 1 If RPM > 14, no correction in WOB a b a 1 80 . n . m . RPM WOB K . WOB 1 . RPM 1 . cos( SR ) t 1 ROP = K . W . h ( x ) ∆ ROP = . W . h ( x ). b ( x ) If RPM < 14, WOB - slide = constant x p 1 f 2 f 2 Ψ 100 . n . CCS c D . tan CCS . D . tan( BR ) t 1 B B WOB WOB WOB + + ∆ ∆ ∆ p p p b 3 b 3 ∆ ∆ − − − n BG n i 2 i 3 i 4 BG ∑ ∑ where, constant = − − W = 1 a ∆ W = 1 a ∆ BG = Ca WOB . RPM . CCS . ABR BG = Ca WOB . RPM . CCS . ABR 3 f 3 f 3 i i i i i i i i 8 8 i = 2 i = 2 Sp. Gravity Abrasiveness GR (API) JSA b ⋅ ( HSI ) 2 Sand 2.6 1 10-30 ⋅ ⋅ HHP [ Q . P / 1714 ] 2 2 D D B B HSI HSI = = = = ⋅ ⋅ B B Silt Silt 2.67-2.7 2.67-2.7 0.85 0.85 50-70 50-70 h h ( ( x x ) ) = = a a ( ) 2 2 A π c [ / 4 D ] ROP 2 B B Conglomite 2.4-2.9 0.71 10-140 Dolomite 2.84-2.86 0.65 <30 − ( 1 . 02 N b x 0 . 02 ) Limestone 2.7 0.57 <20 RPM b ( x ) = Shale 2.4-2.8 0.11 80-300 0 . 92 RPM Coal, bituminus 1.35 0.1 20 n t = avg. no. of inserts contacting rock ∆ p = differential pressure N b = number of blades m = no. of inserts penetrations per revolution RPM = surface RPM ∆ BG = cumulative bit wear = chip formation angle Ψ WOB = weight on bit Ca = bit wear coefficient RPM = top-drive / surface RPM ABR = abrasiveness constant K 1 , a 1 ,b 1 ,c 1 ,a 2 ,b 2 ,c 2 ,a 3 ,b 3 – empirical constants SR = PDC cutter side rake angle HSI = horsepower per sq. inch CCS = confined compressive strength JSA = junk slot area D B = diameter of bit HHP = hydraulic horsepower BR = PDC cutter back rake angle Q = pump flow rate W f = bit wear function P B = bit pressure drop h(x) = hydraulic efficiency function A B = bit face area b(x) = N b effect function 6
Methodology (3/3) 7. CCS to UCS, Pc = confining pressure CCS and Young’s UCS = unconfined compressive strength UCS = b 1 + a s Pc . s CCS = confined compressive strength modulus Ec = Young’s modulus calculation Ec = CCS . a .( 1 + Pc ) b E E a s ,b s ,aE,bE - empirical constants from laboratory triaxial test data for development TOPS 7
Input Data Compilation (1/4) 1. Sheave HL, HL- 2. Wellbore 3. Downhole wt of hook, HL friction coefficient Weight on Bit after SPP (µ), Calculated HL (DWOB) Drill string data Rig/mud motor data Survey data Depth based data Time based data • Depth in, Depth out • Wt of hook / top drive • MD • MD, ROP, WOB, RPM • Bit depth, Depth • Pipe ID, OD • No. of lines, sheave ? • Inclination • HL, Pump vol., ?P • HL, WOB, RPM • Nominal weight • Depth-in, -out, Mud • Angle • SPP/Pump P, MWD • Pump vol. / Flow in • Length motor const. Gamma • SPP/Pump P, ROP Source: Daily Drilling Report/s 8
Input Data Compilation (2/4) 1. Sheave HL, HL- 2. Wellbore 3. Downhole wt of hook, HL friction coefficient Weight on Bit after SPP (µ), Calculated HL (DWOB) Drill string data Rig/mud motor data Survey data Depth based data Time based data • Depth in, Depth out • Wt of hook / top drive • MD • MD, ROP, WOB, RPM • Bit depth, Depth • Pipe ID, OD • No. of lines, sheave ? • Inclination • HL, Pump vol., ?P • HL, WOB, RPM • Nominal weight • Depth-in, -out, Mud • Angle • SPP/Pump P, MWD • Pump vol. / Flow in • Length motor const. Gamma • SPP/Pump P, ROP Source: Mywells.com 9
Input Data Compilation (3/4) 4. Sliding- 5. ROP Models 6. CCS to UCS DWOB, Relative for a and Young’s abrasiveness Rollercone/PDC modulus calculation drill bit calculation • Jet1-8 diameter Drill bit data Laboratory triaxial data • No. & Dia. of cutters • Back & side rake angle • Bit no., Type, Dia. • Effective confining • Cutter thickness • IADC Code pressure • Junk slot area • Depth in, Depth out • Effective confining • No. of blades • Wear in, Wear out strength Source: Mywells.com Source: Mywells.com
Recommend
More recommend
