Grain LNG: A Collaborative Approach To LNG Terminal Business Performance Improvement

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Grain LNG: A Collaborative Approach To LNG Terminal Business Performance Improvement Nick Blair, Grain LNG, Commercial Operations Marco Fahl, Honeywell, Senior Consultant 2012 Business Optimization Conference Kuala Lumpur, Malaysia

Agenda Background on Grain LNG CDP @ Grain LNG Collaboration aspects in Planning & Scheduling Models at Grain LNG LNG Terminal & CHP Interaction Annual Unloading Plan Cargo Unloading Scheduling Summary & Conclusions 2 2012 Business Optimization Conference

National Grid Grain LNG: Background 3 2012 Business 3 Optimization Conference

National Grid Grain LNG Vision In considering the way forward with their business processes, Grain LNG considered it to be important to align these to the company vision of; Innovation and Efficiency Continual improvement of processes to add value to staff and customers Operational Excellence Build on our existing knowledge to provide consistently excellent performance Around this vision, Grain LNG engaged Honeywell to scope potential improvements around a large variety of workflows 4 2012 Business Optimization Conference

CDP @ Grain LNG - Project Timeline June 2010: CDP Scoping Study Identify areas of improvement Dec 2010: Project Kick-Off "CDP Stage-1", incl. Staged Heat Nominations Forecasting (Heatpipe) Implementation Annual Unloading Plan (AUP) Jan 2011: Project Kick-Off "CDP Stage-2", incl. Marine Scheduling etc June 2011: SAT Stage-1 Scope July/Aug 2011: Scoping Study "Commercial Operations Business Processes" Sept 2011: SAT Stage-2 Scope July 2012: Project Kick-Off Stage-3 (Cost Allocation) Nov 2012 (Plan): SAT Stage-3 Scope 5 2012 Business Optimization Conference

Introducing Capacity Distribution Planner (CDP) Provides interactive, structured and collaborative environment for decision support Create model of production and distribution Provides daily prediction of key values such as production and inventories Enable view of plan and historical values Manage plan revisions and workflow Easy to Use Familiar Microsoft Office UI concepts Conditional formatting to flag deviations Supports what if capabilities Highly Configurable and Extensible Calculations configured similar to Excel Create views for specific users Easy to add plug in capabilities Foundation for Supply Chain Solution Integrates easily with other systems 6 2012 Business Optimization Conference

Capacity and Distribution Planner (CDP) Excel-like, but without the inherent limitations and consistency problems Flexible models Easy to use, navigate Manages approved plan and tracks actuals against this plan Foundation for broader supply chain solutions Highly extensible and customizable 7 2012 Business Optimization Conference

CDP within the Grain LNG system Landscape 8 2012 Business Optimization Conference

CDP @ Grain LNG - Solution Overview Cost Allocation Environmental Reporting Commercial Ops Environmental 2-Yrs Heatpipe Forecast Annual Unloading Plan Utilities Tracking Gas Quality Plant Configuration Marine Ops Shift Teams Vessel Unloading Scheduling & Tracking Heat Nominations months ago days ago hours ago now hours ahead days ahead months ahead 9 2012 Business Optimization Conference

External Collaboration: Grain LNG - E.ON 2012 Business Optimization Conference Kuala Lumpur, Malaysia

CHP Scheme Business Drivers LNG vaporisation requires a large quantity of energy, typically around 1.4% of the LNG vaporised E.ON built a 1.2 GW CCGT power station 3km from Grain LNG. Using excess heat (up to 227MW) from their condensing units to heat hot water for use in the vaporisation process Business Drivers Up to 300,000 tonnes of carbon dioxide saved E.ON benefit from levy exemption certificates for cleaner power generation Grain LNG save up to 170 mcm of customer's gas 11 2012 Business Optimization Conference

Combined Heat and Power Scheme Vaporiser Send-Out Gas LNG Storage Tank Steam Turbine Condenser Gas Turbine LNG Carrier at Jetty Natural Gas In 12 2012 Business Optimization Conference

Importance Of Collaboration The CHP project brings together two separate companies and processes with a combined goal Success is dependant on a wide variety of factors and data, some of which spans over the course of a year The scheduling of requirements needs to consider all relevant information and meet the demands of both teams Daily processes (heat nominations / renominations) to agree on heat requirements and delivery 13 2012 Business Optimization Conference

No. of SCVs Heat Nominations Grain LNG / E.ON heat nomination process Day-Ahead Nominations (indicative-firm-final) In-Day Re-Nominations (up to every hr) Decisions incl Use heatpipe or fuel gas Heat amount Water temperature No of SCVs in CHP mode 1 3 5 2 Gas sendout Figure 1: finding the temperature 4 14 2012 Business Optimization Conference

Internal Collaboration: Annual Unloading Plan and Cargo Unloading Scheduling 2012 Business Optimization Conference Kuala Lumpur, Malaysia

Collaboration is Key - An Example Shipper/Agent Commercial Ops AUP Model Berthing Slots PCAR PCAN Marine Ops Vessel Details / Confirmation Cargo Unloading Gas Quality Confirmation Coordination Ship Captain Utilities Tracking PCAR: Provisional Cargo Acceptance Request PCAN: Provisional Cargo Acceptance Notification 16 2012 Business Optimization Conference

Annual Unloading Plan The Annual Unloading Plan (AUP) is a calendar showing when shippers are allowed to bring in a cargo The allocation of berthing slots is based on a number of heuristic rules including; Number of slots (per contract) Spacing between slots (per contract) Specific shipper-shipper relationships This was a manually intensive task to work out slot spacing in a way to minimise excursion from the rules 17 2012 Business Optimization Conference

Original Workflow for AUP Creation... AUTOSHEET XXX XXX XXX XXX Comments Ships Count 3,4,3,6 4,4,3,6 4,1,3,6 4,3,3,6 4,3,3,2 4,3,3,7 4,3,3,9 Shipper A 55 0.00 55 0 0 100 181 178 173 173 173 176 173 Shipper B 41 0.00 41 0 1 100 140 143 155 152 152 145 152 Shipper C 70 0.00 70 0 2 100 37 40 31 37 37 42 37 Shipper D 29 0.00 29 0 3 100 7 4 6 3 3 2 3 Shipper E 15 0.00 15 0 4 50 0 0 0 10 0 0 0 Shipper F 26 0.00 26 0 5 50 0 0 0 0 0 0 0 6 50 0 0 0 0 0 0 0 22/11/2011 14:06 236 236 0 Total 550 365 365 365 365 365 365 365 Copy & Paste Scheduled 450 184 187 192 192 192 189 192 Comments Pattern E F F Repeats E F A 0 0 8 0 0 B 0 0 0 8 0 0 50 50 100 Q1 1 0 0 Q2 1 0 0 Q3 0 Q4 1 5 2 4 11 Sendo ADJUSTER Day A B C D E F # Arrivals Groups A B C D E 2 F XXX E shipxxx XXX XXX XXX ut 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 6.64 8.90 5.21 12.59 24.33 14.04 31/12/2010 5 2 4 11 A A,B schedules 55 26.64 Int. 55 0 140 Capacity days 6.64 929.1 146,360 192780 5 55 A 0 1 0 0 0 0 21 32 1 0 0 0 0 0 0 1 1 1 1 3 3 01/01/2011 6 3 5 12 B A 41 38.90 41 3 0 102 E 168 8.90 908 143,045 190000 8 41 B 0 0 0 0 1 0 0 0 12 20 8 0 0 0 0 2 0 02/01/2011 0.3636364 4 0.7857143 0.4137931 C 70 45.21 70 4 0 170 F 197 5.21 886.4 139,639 212500 4 70 C 0 0 1 0 6 43 20 0 0 0 0 0 0 0 0 3 0 03/01/2011 1.3636364 5 1.7857143 1.4137931 D 29 12.59 5 29 5 0 73 12.59 918.8 144,737 145000 11 29 D 0 0 0 0 0 1 0 0 0 0 0 4 10 9 3 4 EXCEL-based 0 04/01/2011 2.3636364 6 2.7857143 2.4137931 E 15 24.33 6 6 #VALUE! 160 10 1600 252,048 212500 9 14 E 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 5 0 05/01/2011 3.3636364 7 3.7857143 3.4137931 F 26 14.04 7 7 #VALUE! 160 6 960 151,229 212500 6 25 F 0 0 0 0 0 0 0 4 5 2 0 0 0 0 0 6 1 1 2 2 06/01/2011 4.3636364 8 4.7857143 4.4137931 Schedule based 236 8 195 8 #VALUE! 1.186108 40 E&F 0 0 0 0 0 0 3 7 10 6 5 7 2 0 0 7 0 07/01/2011 5.3636364 0.097561 0.5714286 5.4137931 9 9 on 3-4-3-6-(2) 189.8 8 1 1 2 2 08/01/2011 6.3636364 1.097561 1.5714286 6.4137931 10 10 15 16 17 18 19 20 21 22 23 24 25 26 27 28 9 0 09/01/2011 0.7272727 2.097561 2.5714286 7.4137931 11 11 10 0 10/01/2011 1.7272727 3.097561 3.5714286 8.4137931 Shipping Schedule (across the two 12 jetties) 12 ShipE first in sequence 1 A 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 calculations 1 1 11/01/2011 2.7272727 4.097561 4.5714286 9.4137931 12 instances 13 of 135 ships in 5 days 60 ShipE last in sequence 3 B 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 0 12/01/2011 3.7272727 5.097561 0.3571429 10.413793 A 20 instances 14 of 144 ships in 4 days 80 ShipE single seq 0 C 0 0 0 0 0 0 0 0 0 0 0 0 0 0 13 0 13/01/2011 4.7272727 6.097561 1.3571429 11.413793 14 instances 15 of 153 ships in 3 days 42 ShipE in seq 11 D 2 0 0 0 0 0 0 0 0 0 0 0 0 0 14 1 1 2 3 14/01/2011 5.7272727 7.097561 2.3571429 12.413793 12 instances 16 of 162 ships in 2 days 24 E 0 0 0 0 3 0 0 0 0 1 3 1 5 0 15 1 1 15/01/2011 0.0909091 8.097561 3.3571429 0.8275862 29 instances 17 of 171 ships in 1 days 29 F 0 0 3 4 5 2 0 0 0 0 0 0 0 0 16 1 1 16/01/2011 1.0909091 0.195122 4.3571429 1.8275862 43 instances 18 of 180 ships in 0 days 0 E&F 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17 0 17/01/2011 2.0909091 1.195122 0.1428571 2.8275862 19 19 235 18 0 18/01/2011 3.0909091 2.195122 1.1428571 3.8275862 20 20 Shifted forward one day 7 7 3 5 0 6 19 1 1 19/01/2011 4.0909091 3.195122 2.1428571 4.8275862 21 21 A Deleted Manual tuning & what-if 20 0 20/01/2011 5.0909091 4.195122 3.1428571 5.8275862 B 22 22 B Shifted back one day 6 2 8 3 1 2 ShipE Count days between slots Max days between slots 21 1 1 21/01/2011 6.0909091 5.195122 4.1428571 6.8275862 23 23 C 22 1 1 22/01/2011 0.4545455 6.195122 5.1428571 7.8275862 Day A 24 B C 24 D E F Ships E first Series Single last A B C D E F A B C D E F E&F E&F Sequences E/F SHIP 6.64 8.90 5.21 12.59 14.04 0 0 0 0 0 0 6.64 8.90 5.21 12.59 0.00 14.04 MANUAL FILL D 23 0 23/01/2011 1.4545455 7.195122 0.9285714 8.8275862 25 25 1 0 0 1 1 1 1 1 1 0 1 0 E E 2 1 1 0 1 2 2 2 2 2 1 0 2 1 E F 3 1 1 0 2 3 1 3 3 3 2 0 3 2 2 E 4 analysis 0 0 3 4 2 4 4 4 0 4 0 E 5 1 1 0 4 1 3 5 5 5 4 0 5 1 E Q1 Jan 6 1 1 0 5 2 4 1 6 6 5 0 6 2 E Date 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 328 29 30 31 7 1 1 1 Series 6 3 5 2 1 7 6 6 1 1 3 E Year day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 8 1 1 2 7 4 1 3 2 8 5 0 2 4 E A D B A C A B C D A B C A D 9 1 1 3 1 5 2 4 3 9 7 0 3 5 5 E Feb 10 0 4 2 6 3 5 4 10 0 4 0 0 E Date 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 11 0 5 3 7 4 6 5 11 0 5 0 E Year day 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 12 1 1 6 4 8 1 7 6 12 4 0 6 1 1 E B A C A B C D A B A C D B 13 0 7 5 9 2 8 7 13 0 7 0 E Mar 14 1 1 8 6 1 3 9 8 14 9 0 8 1 E Date 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 15 1 1 9 1 2 4 10 9 15 6 0 9 2 2 E 16 0 10 2 3 5 11 10 16 0 10 0 E Year day 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 A A C B D C A B A D B A C Q2 April C Date 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Year day 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 A D B A C B D A C A B D C A May Date 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Year day 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 B A C D A B C A B D C A B A Jun Date 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Year day 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 D C A B C A D B A C A B D Q3 Jul Date 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Publishing Results Year day 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 C A B A C D A B C A D B C A Aug Date 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Year day 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 B D C A B A C D A B C A D B Sep Date 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Year day 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 A C B A D C A B C A D B A C Q4 Oct Date 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Year day 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 B A D C A B C A D B A C B 18 2012 Business Optimization Conference Nov Date 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Year day 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 A D C A B A C D B A C B A D Maintain alternative versions... Copy & Paste

... has been replaced by CDP-based Workflow for AUP Creation EXCEL-based logic has been encapsulated in CDP calculations Initial AUP available on button-click AUP updates and What-If analysis handled by CDP planning scenarios Data readily available for Users across the organization Integration with other CDP models / external applications 19 2012 Business Optimization Conference

Cargo Unloading Scheduling Following the Phase 3 expansion Grain LNG has two jetties, both with different limitations to the vessel sizes they can accept Historical storage of information mainly paper based and held in individual files per vessel visit Expected offload times based on generic formulae over known vessel performance Recognised requirement for auditable planning and recording of vessel activities 20 2012 Business Optimization Conference

Cargo Unloading CDP Model Inputs AUP & Tide information Vessel Selection from Vessel Database Actual cargo unloading information Outputs/Functionality Detailed timing of cargo unloading based on vessel performance information Constraint evaluation with respect to Back-to-back unloadings Jetty assignments Ship Offloading Reports Forecast / Actual 21 2012 Business Optimization Conference

Summary & Conclusions National Grid Grain LNG looked to build on operational excellence at the terminal with a drive for efficient processes CDP was seen as the ideal platform for managing the CHP scheme and other planning / reporting aspects in operations support The Heat Nomination and AUP models have delivered significant time savings and added clarity to both our customers and CHP partners The collaborative aspects ensure that all parties have the latest view of information; for the CHP scheme this delivers a large cost saving CDP is now seen as an area for growth and integration at Grain LNG 22 2012 Business Optimization Conference

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