(1763) Planning & Estimating Risky Projects: Oil & Gas Exploration. Colin Cropley Matthew Dodds Grant Christie

Transcription

1 (1763) Planning & Estimating Risky Projects: Oil & Gas Exploration Colin Cropley Matthew Dodds Grant Christie

2 PLEASE USE MICROPHONE FOR ALL QUESTIONS AND COMMENTS! 2

3 BIO of Colin H Cropley Colin Cropley is Managing Director of Risk Integration Management Pty Ltd (RIMPL), an Australian company focused on large project quantitative project risk analysis A chemical engineer with over 35 years experience in project management, controls & risk management He has conducted risk management processes, schedule and cost risk analyses and training for many major companies since 2003 He was Chairman of his state Primavera Users Group from 1997 to 2009 and has guest lectured in post-graduate project management courses since 1992 He is a member of AACEI, PMI, AustCost Engg& AustRisk Engg Societies and Society of Petroleum Engineers He helped start Tasar class sailing in Victoria in the 70s & 80s and was twice state champion. He resumed sailing three years ago after a gap of more than 20 years. 3

4 BIO of Matthew D Dodds Matt Dodds is Principal Consultant -Risk Management, Project Controls & Systems Integration at RIMPL He had thorough grounding in project planning and controls and has developed advanced skills in Excel and its programming to build tools to integrate project systems A psychologist, he has utilised his statistical training in developing his expertise in risk management and analysis Matt has developed software tools to enhance and automate the integrated cost & schedule risk analysis (IRA) of large risk models using Oracle s Primavera Risk Analysis He has performed IRAs on projects from ~$2m to > $15bn. Matt is an enthusiastic scuba diver 4

5 BIO of Grant Christie Grant Christie is Vice President, GM Australia / PNG Country Manager for Talisman Energy. He has been promoted from VP PNG Operations since most of the work covered by this paper was done. A chemical engineer from New Zealand with an MBA, Grant worked for Shell (12y), SAIC (2y) and Booz & Co (2y) before joining Talisman Energy in An expert in LEAN and Six Sigma techniques applied to upstream oil & gas exploration and production, Grant leads a PNG exploration team of up to 1,000 employees. Grant was exposed to the use of Monte Carlo analysis at NASA while working with Booz & Co. 5

6 INTRODUCTION OUTLINE OF PRESENTATION 6

7 Presentation Outline The challenges of oil & gas exploration in PNG Why conventional planning & estimating tend to be optimistic How Monte Carlo Method helps counter optimism Conventional Quantitative Risk Analyses versus Integrated Cost & Schedule Risk Analysis (IRA) Use of Unit Operations approach to model PNG Oil & Gas exploration Lessons and outcomes from use of IRA on PNG exploration 7

8 THE CHALLENGES OF OIL & GAS EXPLORATION IN PAPUA NEW GUINEA 8

9 The Challenges of Oil & Gas Exploration in PNG The Oil & Gas Explorer (OGX) has been searching for gas and condensate with its JV partners in PNG since 2009 The multiple licence areas cover almost 30,000km 2 OGX has participated in new gas discoveries and has plans to keep exploring through 2015 for reserves for a proposed LNG project OGX has to deal with large distances, difficult terrain and virgin forests Transportation by river (to forward logistics bases) and helicopter (from bases to seismic and drilling locations) are necessities Up to 10 metres of rain falls over nine months of the year in a significant portion of the licence areas and frequent low cloud bases further restrict flying hours 9

10 Seismic Surveys in Steep Terrain Image at top left shows route of planned seismic lead. Photo at bottom right shows the route on a photograph of the location. 10

11 Seismic Survey Line Preparation Left photo below show the steep terrain in which seismic charge holes are drilled Right photo shows inspection of a line before a shoot. Note protective clothing and footwear 11

12 Prepared Drilling Site During Rig Move The photo shows the K-1 site as construction is completed During the move and assembly of the drilling rig Prior to start of drilling Also shows how thick the jungle around the site is 12

13 Dependency on Helicopters OGX uses civil Chinook helicopters for transporting equipment and materials and other types for personnel moves 13

14 OGX Deterministic Planning Experience in PNG For the first few years, OGX used conventional planning & estimating to set their seismic & drilling targets They found they could usually forecast up to a couple of weeks ahead But beyond that, linear programming (expecting tasks to occur in proportion to their planned durations) tended to break down Schedules slipped, budgets driven by time-dependent costs broke down and targets were not achieved In the face of this pattern, OGX was ready to consider alternative approaches 14

15 WHY CONVENTIONAL PLANNING AND ESTIMATING TEND TO BE INHERENTLY OPTIMISTIC AND HOW TO COUNTER IT USING THE MONTE CARLO METHOD 15

16 Why did planning & estimating fail? Before continuing the OGX story, we need to consider why conventional planning and estimating are usually optimistic We also need to understand how the Monte Carlo Method (MCM) can help: In understanding the reasons for inherent optimism and How the appropriate use of MCM enables us to deal with the causes of inherent optimism 16

17 Why planning & estimating are unrealistic Several reasons are likely contributors: 1) Pressures from proponents to meet preconceived cost and date targets 2) Avoidance of optimism is difficult when single values are assigned to task durations and costs 3) The decreasing likelihood of finishing on time as more activity paths overlap 4) Failure to allow for the effects of risks - events that may occur with variable impact on the project 5) Under-estimating the cost consequences of delays Let s briefly examine these 17

18 First Cause: Meeting Pre-conceived targets This may be one of the most common causes of the failure of planning and estimating The process is so often driven by project owners setting targets both time and cost leading to a top down planning and estimating approach; Instead of developing project schedules and estimates from first principles, considering past experience - bottom up ; When such work is required to Begin with the end in mind 1, the plan is likely to be based on backward-pass late dates with little or no float / contingency and commensurate chances of success 1 Covey, Stephen 1990 The Seven Habits of Highly Effective People 18

19 Second Cause: Single durations versus ranges When asked to say how long it takes to travel from home to the office, most of us would not give a single time. We might say: If traffic is light, I can get to work in 20 minutes If there is heavy traffic and rain, it can take 45 minutes If there is an accident, it might take 70 minutes Most of the time it takes about 30 minutes Project plans consist of many such activities, perhaps thousands Yet when we plan a project, we are required to specify a single duration for every activity If there is pressure to meet a preconceived target date, the chances of an unbiased schedule are low 19

20 Introduce probability to planning using MCM MCM runs projects many times to explore a full range of project outcomes from optimistic to pessimistic Uses a mathematical technique to range and randomise project parameters within pre-selected limits: Selection of task durations within probability distributions: so-called 3-point distributions MCM can also involve addition of activities with preselected probabilities of occurring of < 100% called risk events, as described later 20

21 Use of Scenarios to counter Optimism The 3 point estimates of time or cost -Optimistic, Most Likely and Pessimistic -can be developed by breaking the project into discrete sections and considering each section in turn by workshop/interviews: Record assumptions and sources of uncertainty; Describe in words three scenarios Optimistic, Most Likely and Pessimistic based on the assumptions and Sources of Uncertainty Assign three point values to the durations or costs of all the section tasks or cost line items, based on the above scenarios This approach helps to divorce the duration or estimate line item assignments from the overall target date/cost pressures 21

22 MCM Simulator gives range of outcomes A tool such as Oracle s Primavera Risk Analysis (PRA ex Pertmaster) uses duration or cost ranges to simulate most range combinations and produces probability histograms and cumulative curves such as below 22

23 New Information from Range Analysis From the Histogram and Cum Curve, we can learn: An optimistic finish or cost (~P10 or 10% probable) A likely finish (P50, as possible to finish earlier as later) A conservative finish date (P80 or P90) How likely the project is to finish by the planned (deterministic) date - often quite unlikely The range of probabilistic dates for every activity in the schedule There are also analytical tools that show us what is driving project outcomes So this gives us a means of considering ranges of time and cost rather than single values But there are still two other causes of unrealistic planning 23

24 Third cause: Why Fast Tracking is hard If we have two identical strings of activities and resources to do them, each with a 20% probability of being finished by the target finish date, what is the probability of both being finished by that date? Start Activity A 20% Prob Activity B 20% Prob Finish Date P A x P B = 4% Probability This is known as the Merge Bias Effect (MBE) and it is the reason why it is so hard to finish a project on time when many strings of activities converge into the finish Deterministic planning does not show up this effect, but probabilistic planning does. This is a key reason for using detailed schedules for realistic schedule risk analysis, as summary schedules omit many nodes 24

25 Fourth Cause: Ignoring Risk Events Failure to consider the possible effects of risk events things like the traffic accident on the road to work is the fourth cause of unrealistic schedules and unrealistic cost estimates To deal with this we need to bring in risk events from the risk register and model their probabilistic effect on the project using MCM This cannot be done in planning tools like P6 or MS Project While any one risk event is not certain to occur, over the whole project, provided the process has been thorough, risks in the register should occur in a pattern similar to the forecast Weather uncertainty is another form of important risk input often not considered effectively: Probabilistic weather calendars can be included in MCM models To cover multiple and overlapping causes of interruptions to work Use allows project tasks to move over a seasonal weather backdrop of varying schedule risk 25

26 CONVENTIONAL COMBINED COST & SCHEDULE RISK ANALYSES VERSUS INTEGRATED COST & SCHEDULE RISK ANALYSIS (IRA) 26

27 Integrating Cost & Schedule Risk Analyses We have seen why project plans and estimates based on them tend to be optimistic We have also seen how MCM simulation provides the means to counter those optimistic tendencies Construction-based project costs are usually strongly influenced by time-dependent costs, particularly under schedule overrun conditions It therefore makes sense to combine the analysis of timeuncertainty with cost-uncertainty as argued by Hulett 2 and more recently, by Raydugin 3, albeit in a compromised form We now compare 1) Integrated Cost & Schedule Risk Analysis (IRA) with 2) Separate schedule risk analysis, using a summarised proxy schedule, feeding into a cost risk analysis, stated by Raydugin to be standard practice ibid 2 Hulett, David Integrated Cost-Schedule Risk Analysis, Chapter 11, Gower Raydugin, Yuri Project Risk Management, Pages , Wiley

28 Integrated Cost & Schedule Risk Analysis (IRA) Combining time and cost uncertainty makes sense: Construction equipment & labour are time-dependent costs Project materials and equipment are time-independent costs Risk events with time and/or cost impacts will affect costs Overlaying the project estimate on the schedule enables simultaneous MCM analysis of all time & cost uncertainties by: Splitting fixed and variable costs Linking cost item hammock tasks to their driving tasks Adding risk events with time and/or cost impacts Applying probabilistic weather calendars (not to hammocks) IRA enables time drivers of project cost to be: Identified and ranked with cost uncertainties Included in risk optimisation by Quantitative Exclusion Analysis (systematically excluding each uncertainty contributor and re-running the MCM simulation to measure the probabilistic time and cost contribution by difference) 28

29 IRA gives simultaneous time and cost analyses Using PRA with supporting software to facilitate the IRA methodology enables simultaneous time and cost analysis of L3 Integrated Master Control Schedules and detailed cost estimates for major & mega projects 70.0 Yandimoomba Schedule Risks A First Product : Finish Date % 29 Jun 16 95% 27 Dec 15 Yandimoomba Schedule Risks Entire Plan : Cost $8,051,127, % $25,955,604,746 95% $18,354,892,326 90% 31 Oct % $16,385,327, % 10 Sep 15 80% 10 Aug % $15,016,401,365 80% $13,930,930,162 75% 06 Jul 15 75% $12,925,428, % 08 Jun 15 65% 14 May % $11,964,671,858 65% $11,257,058,900 Hits % 18 Apr 15 55% 25 Mar 15 50% 28 Feb 15 45% 05 Feb 15 40% 10 Jan 15 35% 16 Dec 14 Cumulative Frequency Hits % $10,396,676,291 55% $9,671,103,792 50% $8,807,273,046 45% $8,075,476,734 40% $7,399,934,946 35% $6,669,723,686 Cumulative Frequency % 17 Nov 14 25% 14 Oct 14 20% 14 Sep % $5,761,145,946 25% $4,964,691,030 20% $3,882,577, % 10 Aug 14 10% 21 Jun % $2,640,383,242 10% $1,212,038,813 5% 07 Apr 14 5% ($881,768,079) Jun Oct 15 Distribution (start of interval) 0% 27 Oct 13 0 $0 $20,000,000,000 Distribution (start of interval) 0% ($9,364,585,128) 29

30 IRA enables integrated analysis of drivers Simultaneous analysis of time and cost using risk factors, risk events and time and cost uncertainties enables combined rankings of delay and cost drivers using Quantitative Exclusion Analysis 30

31 Separate Schedule & Cost Risk Analyses Due to limitations of earlier MCM tools and the reality that almost always, different teams look after planning and estimating, a separate approach to combining schedule and cost risk analysis has evolved The following summarises Raydugin s description ibid : SRA using a summarised schedule ( Level 1.5 ) Transferring a cost allowance for schedule uncertainty to a separate CRA assuming an average burn rate ($/day) The cost allowance can be the same discrete distribution as for the duration uncertainty if schedule and cost WBS are synchronised for a small number of major deliverables Both above analyses may include risk events 31

32 Objections to Separate SRA & CRA approach Separating SRA from CRA prevents the analyses from quantifying and ranking the cost consequences of the various delay drivers and risks in the SRA with the cost drivers and risks in the CRA This lack of integrated cost driver rankings prevents effective risk optimisation by the project team The use of small summary models, even for large and complex projects, ignores the Merge Bias Effect and gives falsely optimistic schedule and cost results Raydugin himself concludes Only integrated cost and schedule analysis can guarantee adequate representation of schedule-driven costs ibid 32

33 Objections to IRA overcome Raydugin argues that IRA is unworkable because: working with the level 3 or 4 project schedule of hundreds or even thousands of normal tasks and applying hundreds or thousands of cost line items is impractical the estimate and schedule structures are almost always misaligned preventing such integration The IRA methodology and supporting software around PRA have been developed to handle large schedules and estimates in manageable analysis times This includes: dealing with cost/schedule structural misalignments ranging large numbers of tasks and costs by percentages at the area/discipline level correlating related risk factors as well as groups of tasks and costs assigning fixed and variable cost splits by groups where appropriate assigning probabilistic weather calendars at summary levels using a macro-driven spreadsheet and pivot tables to organise tasks and estimates, apply ranging and directly load ranges into PRA 33

34 USE OF UNIT OPERATIONS APPROACH TO MODEL PNG OIL & GAS EXPLORATION 34

35 Creative use of IRA by OGX Generic Template Projects were planned for Unit Operations of Oil & Gas Exploration, comprising: Seismic Surveys Drilling Pad Site Construction Moving and Assembling Drilling Rig Drilling Each Unit Operation was carefully planned and workshopped*, with Typical duration ranges, Overlay of fixed and variable costs, appropriately split and ranged Risk Events with time and cost impacts mapped into the schedule Typical probabilistic time and cost forecasts were produced from IRA modeling These were then available for combining & customising for real projects *(except Moving & Assembling Drilling Rig which was only planned) 35

36 Use of IRA for Rig Downtime Analysis The generic sub-projects and tasks were combined and linked at various probability levels to explore whether any gaps opened up QTR 2 QTR QTR 4 QTR 1 QTR 2 QTR 3 QTR 4 QTR 1 QTR 2 QTR 3 QTR 4 Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Southern Seismic Campaign P10 P50 P90 Southern Seismic Campaign 2012 Southern Seismic Campaign 2012 Southern Seismic Campaign 2012 Southern Seismic Campaign 2012 Seismic Interpretation P10 P50 P90 Seismic Interpretation (261-Lead) Seismic Interpretation (261-Lead) Seismic Interpretation (261-Lead) Seismic Interpretation (261-Lead) Construct Well Sites P10 P50 P90 Puk-Puk-2 Aiema-1 Platypus-1 Langia-Nth Aiema Lead 285-F Muruk-East Puk-Puk-2 Aiema-1 Platypus-1 Langia-Nth 261-Lead 285-F2 Aiema Muruk-East Puk-Puk-2 Aiema-1 Platypus-1 Langia-Nth 261-Lead Aiema F Muruk-East Puk-Puk-2 Aiema-1 Platypus-1 Langia-Nth Aiema Lead 285-F Muruk-East Move Rig / Drill = Drilling Puk-Puk- Aiema-1 Platypu Langia- Aiema F Muruk Puk-Puk-2 Aiema-1 Platypus-1 Langia-Nth Aiema Lead 285-F Muruk-East P10 P50 P90 Puk-Puk-2 Aiema-1 Platypus Langia- Aiema Lead 285-F Muruk-East Puk-Puk-2 Aiema-1 Platypus-1 Langia-Nth Aiema Lead 285-F Muruk-East Puk-Puk-2 Aiema-1 Platypus Platypus-1 Langia-Nth Aiema Lead 285-F Muruk-East Puk-Puk-2 Aiema-1 Langia-Nth Aiema Lead 285-F Muruk-East Puk-Puk-2 Platypus- Langia-Nth Aiema Lead 285-F Muruk-East Puk-Puk-2 Aiema Aiema-1 Platypus-1 Langia-Nth Aiema Lead 285-F2 Muruk-East The above table shows increasing (expensive) rig down time as planning becomes more pessimistic, due to resource bottlenecks, enabling optimising The above planning shows drilling activity that did not eventuate, including wells with conceptual names only. However it demonstrated the need for de-bottlenecking 36

37 Risked Drilling & Un-risked Rig Move Outcomes The following tabulated results compare forecasts with actual results for two recent wells drilled by OGX. The Rig Move planning and budgeting was done deterministically The drilling planning and estimating was based on probabilistic forecasting In both cases, the actual drilling costs were lower than planned probabilistically, but the actual rig move costs were greater than planned deterministically K-1 Well: Rig Move (Un-risked) Act/Plan Drilling (Risked) Act/Plan Actual Plan Actual % Plan Actual % Cf Forecast Total days 21 days 40 days 190% 51 days 53.8days 105% P87 Total cost $5.79m $7.9m 136% $16.2m $15.4m 95% P45 M-1 Well: Rig Move (Un-risked) Act/Plan Drilling (Risked) Act/Plan Plan Actual % Plan Actual % Total days 35 days 45 days 129% 31 days 29.5days 95% Total cost $8.23m $9.63m 117% $10.2m $9.7m 95% 37

38 Risked Seismic Survey Outcomes The following tabulated results compare forecasts with actual results for two recent seismic survey campaigns by OGX The seismic planning and estimating was based on probabilistic forecasting In both cases, the actual survey costs were slightly higher than planned, but still within capital governance tolerances. Durations in both cases were higher than planned P90 values. Southern Blocks: Seismic Survey Act/Plan Plan (P90) Actual % Total days 152 days 168 days 111% Total cost $39.5m $39.8m 101% PPL 239 (Highlands 2013): Seismic Survey Act/Plan Plan Actual % Total days 69 days 80 days 116% Total cost $15.1m $15.9m 105% 38

39 LESSONS AND OUTCOMES FROM USE OF IRA ON PNG EXPLORATION 39

40 Outcomes from IRA use by OGX Planning and estimating have become more realistic as they are generally based on conservative probability levels of time and cost contingency Discipline managers aim to achieve P50 time and cost results or better Some examples of the use of the IRA analysis by OGX: Seismic survey outcomes are now much closer to initial planning Drilling rates are based on probabilistic ranges statistically derived from real data obtained from previous wells drilled in PNG and distinctions are made between normal progress ranges and delays due to risk events Site construction is planned on the basis of optimising cut and fill volumes and weighing up costs of flying in an extra grader or working a grading night shift versus benefits of a better drilling location or a faster completion of the site, critical in such poorweather conditions OGX is planning and meeting its targets more consistently, helped by use of IRA 40

41 QUESTIONS/COMMENTS? (PLEASE USE MICROPHONE) 41