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Knowledge Glossary

Oil & Gas Glossary

Energy Industry Terminology Glossary

Aucerna is the global leader in modern software solutions for upstream oil and gas. This glossary of terms was built from thousands of hours of development, drawing from our team’s industry knowledge and combined experience. These glossary terms are instrumental to understanding our industry and to having a common language that connects each and every one of us.

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This is an abbreviation for “Abandonment Expenditure”.  Some companies use this term to refer to costs associated with abandonment and reclamation of oil and gas wells and facilities at the end of their useful lives.
Consolidating data (usually forecast data) that exists at a more granular periodicity (monthly, quarterly) into yearly quanta.   At its most basic, this simply involves adding up all the monthly, etc., data into 12 month (for example) chunks for use in annual analysis.  Complications can arise around assumed start dates within a year, data that crosses annual borders, etc.
An “Aspired Portfolio” paints a picture of what the portfolio should look like vs what it currently is.  Describing the gaps between the current and  aspired portfolios helps illuminate investment opportunities that need to be added or removed from the existing portfolio.
In the context of planning, an asset is anything the can be included on the “Property, Plant and Equipment” line of the Balance sheet.  It includes mineral rights, wells, facilities and intellectual property,  The collection of discrete assets that are under the stewardship of a specific asset team are also collectively referred to as an Asset. This is the most common use of the term “Asset” in oil and gas planning.
Asset Development Plans (ADPs) are technical and economic plans for assets that are used in the business planning process. ADPs incorporate high level strategies to inform the business of the potential costs, timing, volumes, and value of specific strategies. One or more field development plan (FDP) may inform the asset development plans if the level of detail regarding reservoir simulation, flow assurance and feasibility is required for the level of investment by the business. An asset development plan may have less technical detail than a typical field development plan but cover a broader scope including commercial and strategic factors as well as a number of scenarios rather than a single plan.
Asset Planning is a term used to describe the activities associated with preparation of a field development plan (FDP) and an asset development plan.  One or more such plans may be prepared by a single asset team, depending on the scope of their responsibility and the requirement for multiple scenario generation.
An Asset Team is a multidisciplinary group of professionals that are charged with the stewardship of a particular collection of an organizations oil and gas assets.  This collection is often defined geographically or as a group of specifically named fields and/or licences. The group of assets under the management of a given team are usually self contained, with very little or no overlap with other asset teams.  An asset team is responsible for developing the asset development plan (ADP) and the field development plan (FDP) and managing the execution of them, at times utilizing shared services such as Drilling or Facilities specialists.
A Business Plan is a corporate document that sets out the operational and financial objectives of a company, describes the plan of activities and associated expenditures to achieve these objectives over a specific period of time. It summarizes the portfolio of investments selected during the business planning process. A Business Plan includes a set of time series data that describes the set of projected activities and their expected results over the specific period of time.  It typically contains time series data for: CapEx  OpEx  Production Reserve Additions  Non-Oil and Gas Revenue (i.e. fees) Royalties and Government Burdens and Taxes Prices Calculated results from these inputs are typically: Total Revenue Before Tax Cash Flow After Tax Cash Flow DD&A Deferred Taxes Net Income Less commonly, it includes Financial elements such as: Debt Interest G&A Costs Hedging Gain/Loss Share Purchases / Sales In all cases, the time series data above are aggregated, multiplied, ratioed, and otherwise manipulated to generate scalar metrics that are of interest to management. The time frame of the analysis is what is used to give it a name.  A Business Plan that covers 12-18 months of activity is usually called a Budget.  A Business Plan that covers 3-10 years of activity is usually called a long range plan.
Business Planning is a decision making process intended to select a portfolio of investment decision unit that best achieve a set of strategic goals and objectives. Business Planning leverages technical models and economic assumptions provided by others to generate forecasts of portfolio cash flows, operational results and financial metrics. The recommended and approved portfolio, or scenario, is documented to become the business plan.  Neither the actual execution of the selected decision units, nor the stewardship of that execution, are within the scope of business planning.  Business Planning does, however, include lookback analysis, which is used, among other purposes,  to ensure that inputs to Business Planning are as realistic and objective as possible. Business Planning as a process includes the development of a long range plan and the budget.
A Calculated Result is a variable, either scalar or time-series,  that is is generated by submitting input variable and/or other calculated results to a set of calculations.
CapEx is a common abbreviation for “Capital Expenditure”. Capital Expenditure refers to the funds used by a business to acquire, maintain, and upgrade fixed assets. These might include property, plant, and equipment (PP&E) like wells, facilities, buildings, machinery, and office infrastructure. These are usually long-term assets that have a useful life or a productive purpose lasting longer than one year.
Canadian Oil and Gas Evaluation Handbook is also known as COGEH. Please visit the SPE site for more information.
A project is commercial when there is evidence of a firm intention to proceed with development within a reasonable time-frame. Typically, this requires that the best estimate case meet or exceed the minimum evaluation decision criteria (e.g., rate of return, investment payout time). There must be a reasonable expectation that all required internal and external approvals will be forthcoming. Also, there must be evidence of a technically mature, feasible development plan and the essential social, environmental, economic, political, legal, regulatory, decision criteria, and contractual conditions are met. Being economically viable is a necessary but not sufficient condition to being deemed “commercial” It should be noted that while this is a standard definition of commerciality, alternate definitions may apply in the context of certain fiscal regimes or production sharing contracts when defined therein.
Data Aggregation is the process where multiple data elements are gathered and expressed in a summary form to build a new data element. The steps involved in the aggregation process include reading the raw data, transforming it though certain rules and presenting the summarized form. Rules vary widely in complexity from simple arithmetic sums to complex probabilistic aggregations. In general two types of aggregations exist: time aggregation (daily, weekly, monthly, etc.) and spatial or hierarchical aggregation (where time is not applicable or does not change, e.g. daily well production data to daily field production data or subsidiary A assets to holding company B assets) Individual data elements are associated with either Input Variables or Calculated Results.  Time-based aggregations will always result in an  aggregated Input Variable or Calculated Result that has the same name as that of the source data, only at a coarser time scale.  Spatial aggregations will usually result in aggregated Input Variables or Calculated Results that have the same name as that of the source data, but not always.  In some cases, the aggregated Input Variable or Calculated Result variable name may be different from that of the source being aggregated.
The finest granularity at which data is captured; the “elemental” level.  Where it is numerical, it is a single number.  Oil Production for a well-zone is an input variable.  If this data is captured monthly, then a single month’s oil production for the well-zone is a “data element”. See attached diagram.
A decision gate is part of a stage gate process.  The decision gates are the interfaces between the stages.  In its normal progression, the requirements of a project’s current stage are completed and presented for approval (i.e. decision).  If approved, the project passes through the “Decision gate” and into the subsequent phase. The most well known decision gate has been given the name final investment decision (FID).  It represents the transition from the “Define” stage to the “Execution” stage.  Passing through this gate is also known as “Sanction”.
A decision support system (DSS) is usually a computer-based application that collects, organizes and analyzes business data to facilitate quality business decision-making for management, operations and planning. A well-designed DSS aids decision makers in compiling a variety of data from many sources: raw data, documents, personal knowledge from employees, management, executives and business models. DSS analysis helps companies to identify and solve problems, and make decisions.
In relation to an investment, a decision unit is a model of the investment containing sufficient data to make a decision.  The decision must be able to be made on a discrete basis, and may be either dependent or independent of other possible decisions. An organization will usually have decision units with differing scope and size, time frames, periodicity and account level detail depending upon: The decision maker’s position in the organization The type of decision(s) that they are charged with making The maturity of the investment The materiality of certain data elements to the decision The materiality of the decision to the organization.
Descriptive Analytics uses data aggregation and data mining to provide insight into the past and answer: “What has happened?” – Essentially providing insight into the past via summarizing raw data to make it something that is interpretable by humans.
The economic limit is defined as the time (date) when the maximum cumulative net cash flow occurs for a project.  Operating costs should include only those costs that are incremental to the project for which the economic limit is being calculated (i.e., only those cash costs that will actually be eliminated if project production ceases).  The economic limit, should exclude depreciation, ADR (Abandonment, Decommissioning and Reclamation) costs, and income tax as well as any overhead that is not required to operate the subject property.  For a given project, no future development costs can exist beyond the economic limit date.  Interim negative project net cash flows may be accommodated in periods of development capital spending, low product prices, or major operational problems provided that the longer-term cumulative net-cash-flow forecast determined from the effective date becomes positive.
The production rate for a project on the economic limit date. Also known as the “Abandonment Rate”.
Estimated Ultimate Recovery (EUR) are those quantities of petroleum estimated, as of a given date, to be potentially recoverable plus those quantities that have been already produced. For clarity, EUR must reference the associated technical and commercial conditions for the resources; for example, proved EUR is Proved Reserves plus prior production.
The execution team is responsible for execution. It is typically comprised of a multi-disciplinary group defined along functional lines: Preparations / readiness workflows are often split between different functional groups that are all part of the Execution team (e.g. Mineral land, surface land, drilling engineers, completion engineers, manager or coordinator). The capital management team is primary responsible for updating capital forecasts on a monthly basis and providing reports up to management. Production teams monitor actual production and update the technical forecasts. Activity execution may also be split out into multiple teams (e.g. drilling and completion), based on the scale of operations, or consolidated into a single team to manage all activities.
This is a Board/Executive level analysis of current state of the business, strengths, opportunities and exogenous factors.  The output of this exercise is a set of high level goals and boundaries (we will do this;  we will not do that). This strategy, once set, is used to guide all other forms of planning and decision making within the company. According to David A. J. Axxon in “Best Practices in Planning and Performance Management”, this Includes Strategic Thinking and Strategy Making, but excludes strategic planning.  Strategic Planning is work done subsequent to the Executive Strategy Guidance that strives to build a plan that will deliver upon the goals set by it. An example of a Strategy would be “We will be 50% Renewable Energy within 5 years”.
Exploration Expenditure (EXPEX) is a term some companies use to refer to costs associated with exploration activities to differentiate them from other types of costs since often times they have specific accounting and/or fiscal treatment.
Front End Loading (FEL) is a term used to describe the phases of a project that precede execution.  In the context of a stage gate process, it usually covers the Appraise (or Identify), Select, and Define stages. Its purpose is to instill the discipline of doing rigourous analysis and evaluating alternatives/options before the execution begins (when the cost of mistakes are much lower) rather than changing your mind though the execution phases (when the consequences are much higher).  In other words, it is a methodology to ensure that you do your analysis thoroughly before you make decisions, leading to better quality decisions and higher value outcomes. Final investment decision (FID) typically marks the culmination of the FEL process.
FID (Final Investment Decision) marks the point where a project is approved for execution.  In the context of a  stage gate process, it represents the decision gate between the “Define”and the “Execute” stages.
Field development plans (FDP’s) are focused documents describing the technical assumptions and analyses underlying the planned extraction, processing, transportation, and sale of the hydrocarbons in a specified geographical area and geological horizon(s). This incorporates multi-disciplinary inputs including subsurface, wells and production technology, surface facilities and operations. The field development plan in many jurisdictions informs the regulator of the intended plan to develop the resource. A field development plan is therefore prescriptive and is meant to detail the proposed reservoir development, and may include well locations, design and completion, project schedule, operations plan and abandonment. While a single plan is the result from the field development planning process the alternative concepts considered and the rationale for selecting the chosen concept can be included. Each development plan concept being considered will also include its economic implications.  Field Development Plans are often used to support the  asset development plan.
A named variable, either a scalar or a time-series, that is provided by the user.  Capex, Opex and Production (by category and product as appropriate) are typical examples of an input variable. All of the data element in one or more Input Variable are populated to complete a modelling element.
A Lookback is generally understood to be a post-mortem analysis on a completed project or project phase. It is performed with an objective of continuous improvement and learning. At times it is used to uncover the root cause of either favorable or unfavorable variances in outcome vs expectation. Two specific variants of Lookback analysis are: Project Lookback: It can be a “said/did” analysis for a completed Investment – Did I achieve the economics that I justified the Project on? Variants of this may include fixing pricing at those in effect at the time of Project sanction, or allowing them to reflect actual realizations/current forecasts. Investment Driver Lookback: This is a powerful but seldom used technique for understanding the root cause for higher level variances and identifying systemic bias in estimating. It is performed as a “said/did” analysis at the Investment driver level for a population of recently completed projects. Did I estimate CapEx correctly? Reserves? Production? OpEx? It is usually focused on controllable inputs and does not typically consider economic results. See variance analysis for related forms of analysis.
A scalar value used to measure relative performance of a project or a plan.   Example economic metrics include NPV (Net Present Value), IRR (Internal Rate of Return), ROCE (Return on Capital Employed) Examples of operational metrics are F&D Cost (Finding and Development Cost), Volume Efficiency ($/BOE/D), Unit Operating Costs ($/BOE).  These don’t necessarily require economics to be calculated These are also called “Indicators”.
The lowermost point at which a cash flow can be calculated. That means it must have a time-series forecast of one or more of CapEx, OpEx, Production or Revenue.  This may represent a well-zone in some regimes, and it may represent an entire PSC in others.  Other examples might be an operating facility (cost only, or cost and revenue), an R&D investment (CapEx only), a processing fee contract (fee revenue), or a head office G&A burden (cost only).
OpEx is a common abbreviation for “Operating Expenditure” Operating expenditures are the expenses incurred by the company to run its day-to-day operations. This includes the costs to run processing facilities, tariff fees, rent, utilities, and salaries for example.
With respect to resources categorization, this is a optimistic estimate of the quantity that will actually be recovered from the accumulation by a project. If probabilistic methods are used, there should be only a 10% probability that the quantities actually recovered will equal or exceed the P10 value.
With respect to resources categorization, this is a optimistic estimate of the quantity that will actually be recovered from the accumulation by a project. If probabilistic methods are used, there should be only a 10% probability that the quantities actually recovered will equal or exceed the P10 value.
Also known as the 50th percentile, P50 is the “Best Estimate” for a quantity.  If probabilistic methods are used, there will be an equal probability that the actual value of the quantity will exceed the P50 value as fall below it. With respect to resources categorization, this is the best estimate of the quantity of hydrocarbon that will actually be recovered from the accumulation by a project. If probabilistic methods are used, there will be an equal probability that the quantities actually recovered will exceed the P50 value as fall below it.
Also known as the 50th percentile, P50 is the “Best Estimate” for a quantity.  If probabilistic methods are used, there will be an equal probability that the actual value of the quantity will exceed the P50 value as fall below it. With respect to resources categorization, this is the best estimate of the quantity of hydrocarbon that will actually be recovered from the accumulation by a project. If probabilistic methods are used, there will be an equal probability that the quantities actually recovered will exceed the P50 value as fall below it.
With respect to resources categorization, this is a conservative estimate of the quantity that will actually be recovered from the accumulation by a project. If probabilistic methods are used, there should be at least a 90% probability that the quantities actually recovered will equal or exceed the P90 value.
With respect to resources categorization, this is a conservative estimate of the quantity that will actually be recovered from the accumulation by a project. If probabilistic methods are used, there should be at least a 90% probability that the quantities actually recovered will equal or exceed the P90 value.
Pmean is used to describe the Mean (or expected value) of an uncertain variable. With respect to resources categorization, this is the arithmetic mean quantity that will actually be recovered over a large number of projects.
In finance, the “Portfolio Effect” is a term used to describe the fact that the risk in a portfolio of non-correlated investments is lower than the risk of a single investment. The portfolio returns will tend to be less volatile than that of a single investment.   In oil and gas reserves, the “Portfolio Effect” (sometimes called the “Aggregation Effect”) refers to the fact that P90 volumes (or any other probability level other than P50) can not be summed.  Arithmetically summing 10 reserves at the P90 level will not yield a P90 result, because that assumes that the every one of the 10 individual items encounters its 1 in 10 “Low Case”. That would be equivalent to rolling a “1” on a 10 sided die 10 times in a row. The probability of the group total exceeding the sum of the P90 individuals will, in fact, be well over 95%.  If the portfolio is increased to 1000 items, achieving the sum of the P90s is virtually certain.   In practice, the evaluator may choose to relax the level of certainty assumed for individual items, knowing that the aggregate will me more certain than the parts. For example, the Canadian Oil and Gas Evaluation Handbook COGEH cites as a rule of thumb that an evaluator can arithmetically add entity- or field-level deterministic estimates of proved reserves of lesser probability (for instance, a judgmental > P65) that will result in a greater overall probability (P90) at the portfolio or aggregate level, provided enough entities are added together. PRMS 2018 also speaks to this in section 4.2.5.2: Two general methods of aggregation may be applied: arithmetic summation of estimates by category and statistical aggregation of probability distributions. There are typically significant differences in results from these alternative methods. In statistical aggregation, except in the rare situation when all the reservoirs being aggregated are totally dependent, the P90 (high degree of certainty) quantities from the aggregate are always greater than the arithmetic sum of the reservoir level P90 quantities, and the P10 (low degree of certainty) of the aggregate is always less than the arithmetic sum of P10 quantities assessed at the reservoir level. This “portfolio effect” is the result of the central limit theorem in statistical analysis. Note that the mean (arithmetic average) of the sums is equal to the sum of the means; that is, there is no portfolio effect in aggregating mean values.
Predictive Analytics uses statistical models and forecasting techniques to understand the future and answer: “What could happen?” – Essentially assisting to understand the future, providing actionable insights based upon data and providing estimates of the likelihood of a future outcome via combining available historical data and trying to fill in the missing pieces using identified patterns and algorithms.
An Acronym for “Petroleum Resources Management System”.  A widely accepted international standard for the categorization of petroleum reserves and resources.
A PRMS Project is a defined activity or set of activities which provides the link between the Petroleum accumulation(s)s Resources sub-class and the decision-making process, including budget allocation.  In line with PRMS. A project may, for example, constitute the development of a single reservoir or field, an incremental development in a larger producing field, or the integrated development of a group of several fields and associated facilities (e.g. compression) with a common ownership. In general, an individual project will represent a specific maturity level (sub-class) at which a decision is made on whether or not to proceed (i.e., spend money), suspend, or remove. There should be an associated range of estimated recoverable resources for that project. (See also Development Plan.)
A Project is a discrete piece of business that can be described in the most general case with input resources and output results.  Input resources are usually one or more of: capital, expenditures operating expenditures and/or human resources.  Output results may include one or more of: production or injection streams, operating cost increase or decrease, other revenue streams, capital gain or loss, risk mitigation, reserves progression, EH&S outcomes, technical knowledge. A finite collection of Projects, when aggregated, will describe an organization’s forecast operating capex, opex, revenue, burdens and hence operating cashflow. Typical examples of Projects are: development of a well, a single reservoir, or a small field an incremental development in a producing field workover or recompletion of an existing well or wells the integrated development of a field or several fields together with the associated processing facilities a developed field is also considered to be a project G&G activities such as seismic Facilities expenditures for the processing of equity or non-owned production Technical studies or R&D Health, Safety or Environmental management oriented activities Ideally, a Project should be 73269 Single-class (aka Class-clean)Proposed for Adoption.  Should an investment opportunity contain more than one Resource Class or Sub-Class, it should be broken into separate Projects.
Reserve Categories are industry-standard terms that are finer subdivisions of the PRMS “Reserves” Class. Reserve Categories (on the left) are compared to the PRMS terminology (on the right) below:

Common Industry Terminology

PRMS Terminology
Abbreviation Description PRMS Resource Category Equivalence (from Figure 1.1) PRMS Maturity sub-Class (from Figure 2.1)
1P, TP Proven, Total Proved P1, Proved, Low, 1P, P90 Any Reserves sub-class
2P Proved + Probable 2P, Best Estimate, P50, Any Reserves sub-class
3P Proved + Probable + Possible 3P, High, P10 Any Reserves sub-class
P1 Proved P1, Proved, Low, 1P, P90 Any Reserves sub-class
P2, PB, TPA Probable, Total Probable Additional; the difference between 2P and 1P P2, Probable Any Reserves sub-class
P3, PS, TPS Possible, Total Possible Additional; the difference between 3P and 2P P3, Possible Any Reserves sub-class
PDP Proved Developed Producing P1, Proved, Low, 1P, P90 On-Production
PDNP Proved Developed Non-Producing P1, Proved, Low, 1P, P90 On-Production*
PD Proved Developed; the sum of PDP and PDNP P1, Proved, Low, 1P, P90 On-Production*
PUD Proved Undeveloped P1, Proved, Low, 1P, P90 Approved for Development; Justified for Development
PNP Proved Non-Producing; the sum of PNP and PUD P1, Proved, Low, 1P, P90 Any Reserves sub-class

*PDNP, and PD are included in the PRMS “On-Production” sub-maturity class because that class is defined as “Producing or capable of Producing”
Reserves are those quantities of petroleum anticipated to be commercially recoverable by application of development projects to known accumulations from a given date forward under defined conditions. Reserves must satisfy four criteria: discovered, recoverable, commercial, and remaining (as of the evaluation’s effective date) based on the development project(s) applied. Reserves are a subset of the more general term resources.
The process of accounting for hydrocarbon assets by organizing, securing, estimating (including valuing, updating, classifying, categorizing), reconciling, approving, reporting and auditing  reserves) (and potentially also  resources).   Reserves Management is an industry standard term but it is understood that it also encompasses the management of Resources.
Reserve Maturation is a term used to describe the activity of moving hydrocarbon resources between Prospective Resources and Contingent Resources and Reserves.  Within the Reserves Class, it encompasses moving Reserves between Undeveloped and Proven Producing Categories.  Within the Resource Class, it encompasses moving from one maturity subclass to another.
Subdivisions of estimates of resources to be recovered by a project(s) to indicate the associated degrees of uncertainty. Examples of Resource Categories used in PRMS 2018 are: Low/Best/High, P90/P50/P10, 1P/2P/3P, 1C/2C/3C, 1U/2U/3U.  It is represented by the horizontal axis of the PRMS Resource classification framework.
Subdivisions of Resources that indicate the relative maturity of the development projects being applied to yield the recoverable quantity estimates Examples  of Resource Classes defined in PRMS 2018 are: Prospective Resources, Contingent Resources, Reserves (with Sub-classes of On Production, Approved for Development, Justified for Development).  It is represented by the vertical axis of the PRMS Resource classification framework.
The term RESOURCES is intended to encompass all quantities of petroleum naturally occurring within the Earth’s crust, both discovered and undiscovered (whether recoverable or unrecoverable), plus those quantities already produced. Further, it includes all types of petroleum whether currently considered as conventional or unconventional resources.
Risk is the chance that something undesirable will happen.
Sarbanes–Oxley or SOX, is a 2002 United States Federal Law that sets new or expanded requirements for all U.S. public company boards, management and public accounting firms. A number of provisions of the Act also apply to privately held companies. The main impact of this legislation for planning is around the requirement for well documented processes and audit trails for any data subject to public disclosure.  Similar legislation has been implemented in several other countries. Software itself cannot be SOX compliant but it can form part of a SOX-compliant process. For further information, see this wikipedia reference:  https://en.wikipedia.org/wiki/Sarbanes%E2%80%93Oxley_Act
A Scenario is a potential Plan (a specific set of what-and-when) based upon a specific set of assumed conditions (Targets, Constraints, Prices, etc.) at a single point in time. A scenario is best built around a particular view of the future.  Examples might be “Covid-19 Slow Demand Recovery” or, borrowing from Shell, “Sky Scenario: A technically possible, but challenging pathway for society to achieve the goals of the Paris Agreement”. Other scenarios may be built around single constraint assumptions such as Low CapEx, Base CapEx or High CapEx, where each assumption leads to a different set of “what and when”. Useful scenarios can also be built by making assumptions about the top two or three items on the tornado charts  (i.e. the items that have the most impact on the results). It is healthy to push these items to the extreme end of their respective ranges to help understand what program changes would result should those changes materialize. Price is one factor that can be used to frame a scenario AND can also be used as a sensitivity variable in a  sensitivity analysis.  As an example, a scenario can be built around spending cash flow using a LOW price assumption.  This could result in the selection of 50 projects over the next year.  An alternative scenario could be built around spending cash flow using a HIGH price assumption.  That might result in the selection of 80 projects over the next year. Either of these scenarios can then be subject to a sensitivity analysis at various different prices;  if I choose the High price scenario (80 projects), what happens to my cash flow using a low price sensitivity?
Acronym for the US “Securities and Exchange Commission”, the body that governs public disclosure including the requirements for reserves as they pertain to public companies listed on the New York Stock Exchange.
This is a process whereby, for a given scenario,  an input assumption (or assumptions) are changed and the result of that change on the output is calculated. Typical sensitivity variables are commodity price, capital cost, operating cost, production rate, reserves. There are 2 distinct variants of sensitivity analysis used for different purposes: Static Sensitivity Analysis:  In this case, the sensitivity analysis will change the value of an input parameter (or parameters) and recalculate results, altering neither underlying project selections nor project timing (while acknowledging that the sensitivity may cause some wells and projects to be removed from the results if they become uneconomic or their economic limit is reached at a different time).  In this case, the sensitivity analysis will take the project timing as fixed and not alter it.  Since project timing is essentially a primary constraint in “Static” mode, there is no guarantee that other constraints will not be violated in this mode. This type of analysis is used for generation of tornado charts and for assessing the relative sensitivity of a final plan to various input factors. Compared to “Dynamic Sensitivity”, it can result in less precise results. Dynamic Sensitivity Analysis:  In this case, the sensitivity analysis will change the value of an input parameter (or parameters) and recalculate results, altering project timing as indicated.  Project timing shifts may be indicated based upon the sensitivity variables affecting the constraints, or from changes to economic limits in the case of dependent operations (like up hole recompletions).  As in the static case, this sensitivity may cause some wells and projects to be removed from the results if they become uneconomic or their economic limit is reached at a different time.    This type of analysis is commonly used when performing a variance analysis with a simulation or portfolio tool. Compared to “Static Sensitivity”, it will be more likely to honor preexisting constraints and may yield a more precise result. The reader might observe some overlap between a scenario and a dynamic sensitivity, since they may result in a unique “what and when”.  The distinction is that sensitivities (whether they be static or dynamic) are always related to, and generated based upon, a specific scenario.
A multi-disciplinary input and calculation process that utilizes advanced feedback loops, triggers and event interactions to provide decision support through automated selection and/or timing of options to enable a prescriptive analytics approach for the answering of ‘If, Then’ and ‘What should I do’ questions rather than just forecasting on ‘What might happen’ within the  predictive analytics field.
A Project is considered to be single-class (aka Class-clean) if it contains volumes in only a single resource class  and resource sub-class. A data set is considered single-class if it only contains single-class Projects. Projects usually move between resource classes at a decision gate. By definition, a PRMS must be single-class.
Spider charts (also known as radar charts, polar charts, web charts, or star plots) are a way to visualize multivariate data. They are used to plot one or more groups of values over multiple common variables. They do this by giving an axis for each variable, and these axes are arranged radially around a central point and spaced equally. The data from a single observation are plotted along each axis and connected to form a polygon. Multiple observations can be placed in a single chart by displaying multiple polygons, overlaying them and reducing the opacity of each polygon. An example of the type of data that can be easily displayed by spider charts would be the KPI’s of various assets, where the variables shown on the individual axes could be IRR, NPV, F&D cost, Capex, Opex, Average Daily Production Rate… Each axis shares the same tick marks and scale, but the way the range of variable values maps to this scale can vary between the displayed variables. For example, if one variable is IRR, which is measured in %, and another is NPV which is measured in $MM,  these measures need to be converted to a scale of units shared between the axis. Grid lines connect the axes and are used as guideline to make the chart more easily readable
A stage gate process is a project management technique in which an initiative or project is divided into distinct stages, separated by decision points (known as gates). At each gate, continuation is decided by (typically) a manager, steering committee, or governance board. The decision is made on forecasts and information available at the time, including the business case, risk analysis, and availability of necessary resources (e.g., money, people with correct competencies).  Typically the aim of a Stage Gate process is to reduce uncertainty and obtain narrower ranges (more accurate estimates) as one progresses through each stage gate. In oil and gas, a common set of five stage names is Appraise (or Identify), Select, Define, Execute, Operate. Some operators add a sixth stage , Abandon Appraise, Select and Define together are called  Front End Loading (FEL).  The decision gate between Define and Execute is normally called Final Investment Decision (FID) or “Sanction”.
Analysis method that incorporates the random sampling of multiple uncertain inputs, often described by a probability distribution, to generate a range of possible outcomes with associated probabilities of occurrence.
A Strategic Plan is the outcome of the strategic planning process. A Strategic Plan describes a set of investment choices and expected outcomes and associated metrics.  It is intended to provide a viable path by which the executive strategy guidance can be realized. The investment choices are often based upon coarse-grained decision unit designed to be more representative of investment themes than of individual investments.  The Strategic Plan is typically not fine-grained enough to be executable.  Instead, it is used as input to and guidance for the business planning process. A Strategic Plan typically covers a 5- 20 year time frame.
Strategic planning is an organizational management activity used to set priorities, focus energy and resources, strengthen operations, ensure that employees and other stakeholders are working toward common goals, establish agreement around intended outcomes/results, and assess and adjust the organization’s direction in response to a changing environment. Strategic Planning sets targets and allocates resources.  The tangible output of the Strategic Planning process is a strategic plan.  Selecting and scheduling the specific Projects necessary to deliver on that Strategy is within the scope of  business planning.  Strategic Planning should be informed and guided by the results of executive strategy guidance.
Revising scheduled forecast data to a common start date.  The start date is often referred to as “time zero”.   The effect is to remove any pre-determined scheduling of the forecasts to a neutral state, where it is free to be rescheduled by a different process.   An example might be to take existing plan forecast data and return it to start in its first year, so that a portfolio optimization can re-schedule it in response to constraints, etc.  Another use for this is in type well profile analysis, where all production curves are aligned to the same “time zero” start.
Tornado charts are useful for deterministic sensitivity analysis – comparing the relative importance of variables. For each variable/uncertainty considered, one needs estimates for what the low, base, and high outcomes would be. The sensitive variable is modeled as having an uncertain value while all other variables are held at baseline values This allows testing the sensitivity/risk associated with one uncertainty/variable. For example, if a decision maker needs to visually compare 15 variables, and wishes to identify the five that are most impactful, a tornado chart can be very useful. In this example, the top five bars would represent the variables that contribute the most to the variability of the outcome, and therefore may help the decision maker focus their attention and/or guide scenario generation.
The name “type curve” derives from the 1970’s when it was used to describe a log-log dimensionless graph used for pressure transient analysis. Data for these dimensionless graphs derived from analog solutions of the flow equations, and sometimes from reservoir simulation. To avoid confusion, experts like Dr. John Lee recommend a best practice of reserving the term type curve for dimensionless display and analysis of the flow equations. Modern day examples are Wattenbarger’s and Fetkovitch’s type curves for combined linear and boundary dominated flow. It is not uncommon for industry to use the term “type curve” for what we define as a type well profile.  Another related term, type well is a complete modelling element for a typical, repeatable project, often used in unconventional resource planning.
A typical model of a well in an area of interest that has the type well profile (TWP)  or it may simply have the history and forecast from an existing well that is thought to be representative. A type well differs from a TWP in that it may have properties that are in addition to the production profile. Examples of other properties might include the onstream date of production, a capital cost schedule that reflects timing relative to the start of production, a location to define incentive programs and taxation, ownership and other economic information necessary for replication in planning a multi-well program or for portfolio analysis.
A Type Well Profile (TWP) is a rate-time production profile that is intended to represent the expected average profile from new or existing wells in an area of interest having similar drilling and completion design and reservoir characteristics. Most often the TWP is the simple average of similar existing well profiles in the area of interest; however, the rate-time profile may come from another source such as a machine learning algorithm, parametric analysis, rate transient analysis or reservoir simulation. When the supply of similar wells is small, source wells may be drawn from other analogous areas. A TWP may be expressed in a normalized rate-time fashion to extend its utility. An example is where the profile created is the rate per 1000 meters of completed well length. Prior to averaging, source well profiles may be adjusted to more closely match the intended drilling design. The averaging process may be modified using Monte Carlo simulation to represent a specified cumulative probability of occurrence or the stochastic aggregation of multiple wells. A TWP may also include secondary products expressed as a rate-time profile or as a rate ratio of the secondary product to the primary.
A situation in which one knows only the probability of which several possible outcomes has occurred or will occur See also risk.
In a general sense, variance analysis is an “A vs B” comparison.  General examples of “A” and “B” are: Current state of a model element vs a previous state of that same model element Current state of a decision unit vs a previous state of that same decision unit One plan scenario vs another plan scenario One sensitivity vs another sensitivity One version of a plan vs another version of a plan Some specific examples that are common in planning are: Budget vs Actual: This is a common form of variance analysis.  It analyzes the accuracy of my Budget projections. Did I achieve the aggregate plan that I set out to achieve?  Are there actions that I need to take to close any shortfalls vs Budget?   Scenario Analysis:  Best practice Business Planning includes the generation of multiple scenarios.  Comparison of potential scenarios is a key form of variance analysis.
A stored set of modelling element representing specific time-periods, assumptions (such as price, Fx, …) or purposes (Budget, Long Range Plan, Forecast, …). The same modeling elements can exist in multiple Versions.  The information stored in a version can be scalar data, time-series or complex (image, working interest, map, attachment etc) as well a meta-data about the version itself (it’s context, purpose…).

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