This document outlines StrategicFit's process for building custom production forecasting tools to support decision making. The process involves structuring the problem, building a tailored model in phases for feedback, implementing rigorous testing and quality assurance, and providing outputs like field-level forecasts, drivers of uncertainty, and comparisons to actual production. The process is most useful where standard tools are insufficient, building a full model is not worthwhile, and unbiased information and alternatives are needed to support complex decisions around wells, facilities, and uncertainties.

1. Duncan John
Production Forecasting for Decision Making:
+44 7771 562 500
djohn@strategicfit.co.uk
Adam Mitchell
A Process for Building Better Production Forecasting Tools +44 7876 243 480
amitchell@strategicfit.co.uk
FRAMING AND ASSESSMENT AND WHERE IS THIS MOST
OUTPUTS AND RESULTS
STRUCTURING IMPLEMENTATION USEFUL?
Quality forecasting is critical for A model tailored to your needs: Outputs that give real insights: We believe our process has wide
several business needs:  Our models are built around your specifications  Field level well-by-well forecasts: application, but is particularly
and requirements—you do not need to fit your data
 Reserves reporting into inappropriate structures valuable where:
 Underpinning investment and operating decisions  We phase building to maximise feedback:
(debottlenecking, maintenance levels, develop-  Decision making is not supported by the produc-
ment activities) tion forecast
 Assessing the impact of subsurface and surface Test different modelling approaches  Standard software packages are not appropriate
uncertainties on the asset plan  It is not worth investing in setting up and maintain-
 Understanding which questions to ask to ing a reliable dynamic or material balance model, if
challenge operators Create ‘micro-version’ of the model indeed a reliable model is possible at all
 Short, mid and long-term asset planning  Information biases need to be stripped out
 There is an urgent need to get up and running: our
Quality Assurance/Control logic process could take as little as six weeks to com-
The StrategicFit Approach plete:
 We bring decision quality principles to the develop-
Gather initial input assessments  Time series maps of where constraints are defer-
ment of custom built production forecasting tools
ring production: Structuring Building Handover
 Sound reasoning/logic and reliable information are
the focus of production forecasting, but the other (1.5 weeks) (3 weeks) (1.5 weeks)
elements of Decision Quality should not be Expand to full version
ignored...
 Examples include
Rigorous testing  Compartmentalised fields with complex sys-
Meaningful, reliable Clear, genuine
tems of constraints and operational practices
INFORMATION PREFERENCES  Reservoir engineering, production planning and  Mature asset with a limited window of opportu-
appraisal engineering/project team members are nity
 Tornado diagrams reveal key drivers of future pro-
all involved.  Regional forecasting across multiple assets
Creative, doable Sound duction; Monte Caro analysis gives a true basis for
ALTERNATIVES REASONING P10-P50-P90 curves:
Flexibility and Transparency
 Our models are agnostic to existing tools and
About StrategicFit
Useful Commitment to even reservoir models can be used with manually
FRAME ACTION inputted decline curves
 Our models are implemented in Excel, with full
transparency to the underlying logic—not a ‘black
box’ optimization tool
Who we are:
 We do not focus on unrealistic optimums but on
 A strategy consulting firm with extensive experi-
identifying complex production dynamics and un-
ence of working with E&P companies
certainties to support your decision making proc- Probabilistic Analysis  We use well established decision analysis tech-
ess Go beyond the deterministic and acquire a long-
 niques to focus on what matters to improve the
Examples Decisions Uncertainties term as driven by uncertainty ranges and decisions quality of decisions
 Optimising performance under uncertainty is far  Our team is very flexible in terms of sizes of project
Wells  Prioritisation  Flow rates  Compare to actual production; waterfalls reveal
superior to optimising given a point forecast we take on and we don’t carry the overhead of lar-
(under constraints)  Cut-off rates sources of difference:
 Accurate uncertainty range assessments are ger consulting firms
 Well activity type  Well failures
crucial, and we have experience challenging
(e.g. workover,  Timing of activities
sources of bias: Our clients:
new well)  Probability of suc-
 Drilling sequence cess
 Number and type
Cognitive Bias Motivational Bias
of rigs  Anchoring  Misaligned incentives
 Representativeness  Prevailing customs
Facilities  Planned mainte-  Uptime
nance  Timing  Unstated assumptions
 De-bottlenecking  Constraints