ESTIMATED RECOVERY GENERATOR DATASET



Similar documents
REU Houston 2014 Enhanced Reserve and Resource Estimates. Trevor J. Rix Senior Engineer August 12, 2014

CENTURY ENERGY LTD. (the "Company") FORM F1 STATEMENT OF RESERVES DATA AND OTHER OIL AND GAS INFORMATION

COGA Colorado Oil & Gas Industry Tax Whitepaper

STANDARD FOR ASSESSING MINERAL RIGHTS

Kiln Drying Planning Tool. User Guide

Enhanced Oil Recovery (EOR) in Tight Oil: Lessons Learned from Pilot Tests in the Bakken

RAGING RIVER EXPLORATION INC. ANNOUNCES 2015 YEAR END RESERVES AND UPDATED 2016 GUIDANCE

ARGUS Developer v Product Release Notes Document Version 1.02 August 7th, 2014

Third Quarter 2015 Swift Energy Company November 5, 2015

Evaluating Trading Systems By John Ehlers and Ric Way

Ad Hoc Advanced Table of Contents

Comparison of State Severance Taxes on Oil and Gas Utah and Selected Oil and Gas Producing States

Oil & Gas Property Evaluation: A Geologist s View* R.W. Von Rhee 1. Search and Discovery Article #40640 (2010) Posted November 30, 2010.

Industry Environment and Concepts for Forecasting 1

Oil & Gas UK I N D E X. December 2009

ANALYZING NETWORK TRAFFIC FOR MALICIOUS ACTIVITY

A vector is a directed line segment used to represent a vector quantity.

D1.1 Service Discovery system: Load balancing mechanisms

Alaska s Oil Production Tax: Comparing the Old and the New By Scott Goldsmith Web Note No. 17 May 2014

SECTION TTT PART II C: DIFFERENTIALS CRUDE OIL AND REFINED PRODUCTS PART II: SPECIFIC STANDARD TERMS FOR SWAP FUTURES CONTRACTS:

March 2015 MEGATRENDS IN THE OIL AND GAS INDUSTRY

Spreadsheets and Laboratory Data Analysis: Excel 2003 Version (Excel 2007 is only slightly different)

Trilogy completed the sale of its Dunvegan oil assets in the Kaybob area for net proceeds of $45 million.

Marcellus Shale in Pennsylvania: A 2,600 Well Study of Estimated Ultimate Recovery (EUR) 2015 Update

Chapter 7 - Find trades I

PRESS RELEASE. November 12, 2013

6 Steps to Faster Data Blending Using Your Data Warehouse

NEWS RELEASE CHINOOK ENERGY INC. ANNOUNCES ITS DECEMBER 31, 2015 RESERVES AND PROVIDES OPERATIONS UPDATE

Understanding EMC Avamar with EMC Data Protection Advisor

DATABASE DESIGN AND IMPLEMENTATION II SAULT COLLEGE OF APPLIED ARTS AND TECHNOLOGY SAULT STE. MARIE, ONTARIO. Sault College

PRIVATE EQUITY INSIGHTS

LAREDO PETROLEUM ANNOUNCES 2015 SECOND-QUARTER FINANCIAL AND OPERATING RESULTS

Quality of Service versus Fairness. Inelastic Applications. QoS Analogy: Surface Mail. How to Provide QoS?

Return on Investment (ROI)

CLARITY PPM TIPS, TRICKS, and TERMINOLOGY

Reliability Modeling Software Defined

Analysis Methods. Traditional. Home > Theory & Equations

SPSS: Getting Started. For Windows

How To Compare N.Dakota To North Dakota

Flagship Fleet Management, LLC. Replacement Analysis

Procurement Planning

Regression Clustering

ILLIQUID ALTERNATIVE ASSET FUND MODELING. Dean Takahashi Yale University Investments Office. Seth Alexander Yale University Investments office

Cash Flow Projection for Operating Loan Determination

Study Assesses Shale Decline Rates

OIL AND GAS TAXATION COMPARISON

OANDA FXTrade Platform: User Interface Reference Manual

3/11/2015. Crude Oil Price Risk Management. Crude Oil Price Risk Management. Crude Oil Price Risk Management. Outline

Econ 330 Exam 1 Name ID Section Number

Absorbance Spectrophotometry: Analysis of FD&C Red Food Dye #40 Calibration Curve Procedure

Investments Analysis

Constraints Propagation Techniques in Batch Plants Planning and Scheduling

KM Metering Inc. EKM Dash User Manual. EKM Metering Inc. (831)

Heat Map Explorer Getting Started Guide

Sitecore Health. Christopher Wojciech. netzkern AG. Sitecore User Group Conference 2015

Investment Decision Analysis

Chapter 12. Aggregate Expenditure and Output in the Short Run

REPORTING OF RESERVES AND RESOURCES IN CANADA DISCLAIMER

18: Enhanced Quality of Service

Using Excel (Microsoft Office 2007 Version) for Graphical Analysis of Data

FlowMergeCluster Documentation

IBM SPSS Direct Marketing 23

Educational Note. Premium Liabilities. Committee on Property and Casualty Insurance Financial Reporting. November 2014.

Web Dashboard. User Manual. Build

Study Questions 8 (Keynesian Model) MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

Engineering note EN-47

IBM SPSS Direct Marketing 22

Easily Identify Your Best Customers

Intro to Excel spreadsheets

Commodities Capstone

Multi-service Load Balancing in a Heterogeneous Network with Vertical Handover

Lectures 5-6: Taylor Series

Microsoft Access 3: Understanding and Creating Queries

F9 Integration Manager

Optimization: Optimal Pricing with Elasticity

SPARE PARTS INVENTORY SYSTEMS UNDER AN INCREASING FAILURE RATE DEMAND INTERVAL DISTRIBUTION

MATHEMATICAL TRADING INDICATORS

Options Scanner Manual

Credit Card Market Study Interim Report: Annex 4 Switching Analysis

We first solve for the present value of the cost per two barrels: (1.065) 2 = (1.07) 3 = x =

How To Model A System

Accurately and Efficiently Measuring Individual Account Credit Risk On Existing Portfolios

Using BlueHornet Statistics Sent Message Reporting Message Summary Section Advanced Reporting Basics Delivery Tab

Predictive Modeling Using Transactional Data

Query. Training and Participation Guide Financials 9.2

Blender Notes. Introduction to Digital Modelling and Animation in Design Blender Tutorial - week 9 The Game Engine

Transcription:

ESTIMATED RECOVERY GENERATOR DATASET Available via DI Desktop Overview In Drillinginfo, the Estimated Recoveries Generator dataset within DI Desktop offers pre-generated Estimated Ultimate Recoveries (EURs) and decline curve parameters for all active entities. The EURs in DI Desktop are referred to as technically recoverable resources in that they are solely based on historical production volumes reported by state and federal agencies. For example, this means no economics are considered (for example, taxes, capital expenses, operator expenses, oil and gas prices), no completion data is considered (for example,. half-frac lengths), and currently, type curves are not used as analogs for calculating forecasts. When production data arrives from the governing agency, it is loaded into the DI Desktop ingestion database. From here, the production stream is analyzed, checking if the volumes have changed from the last time production was reported from the state and if the production stream has more than six months of production. This systematic process occurs multiple times per month, thus delivering an upto-date set of forecast data for each entity in the DI Desktop database. Forecasting Methodologies Drillinginfo has teamed up with Energy Navigator in Calgary, Alberta, Canada and has licensed their forecasting tool, Value Navigator, to perform forecasts on each eligible producing entity in the DI Desktop database. In particular, Drillinginfo utilizes only the core Value Navigator s technology: the Auto-Fit calculator. The Auto Fit software is designed to find all decline Segments within the data set, where a segment is a unique decline trend based on one of the three Arps equations: exponential, harmonic and hyperbolic. Once all of the Segments have been defined, the best is selected based on decision analysis that includes how close the segment is to the current date, how many data points are in the segment, and how good is the fit. -Energy Navigator Note: For the purposes of this document, an entity is defined as the level at which production is reported for an individual historical stream of production. This level varies due to the reporting procedures enforced by each state agency. Therefore, production could be reported at the completion, well, lease, unit, field, or block levels, depending on the state and country. In some states, which do not exclusively report at the well/completion level, well header data allows a mapping between wells and lease/unit level production, therefore allowing the production to be allocated amongst the wells. This is most commonly seen in Texas and Oklahoma. In Texas the Estimated Recovery Generator processes both lease-level and allocated records. In fact, the processor is not state-specific; meaning every entity in DI Desktop is forecasted by the processor, as long as its production stream is eligible for forecasting.

A detailed description of the logic process for obtaining the Auto Fit can be read in Energy Navigator s Auto Fit Whitepaper, which is available upon request from Energy Navigator. At a high level, the previous description of the Auto Fit software can be visualized as seen in Figure 1. Value Navigator automatically chooses the type of decline that is most appropriate for your production stream. The decline equations are based on the Arps equation. There are three types of declines in Value Navigator: 1. Exponential 2. Harmonic 3. Hyperbolic Figure 1: Auto Fit segmentation illustration. Best-Efforts and Full Fits The Auto Fit calculator can be instructed to fit the entire production history or segment the production into trends and determine the best trend to use in the forecast. We refer to these fits as full vs. best-efforts. Drillinginfo has decided to include all forecast data from both best-efforts and full fits in the underlying DI Desktop database; however, we pick and choose from the best and full values for display within the DI Desktop interface. The reason for this is that B-factor and initial declines are typically referred to at time zero (Figure 2). Therefore, wherever B-factor and initial decline are displayed in the DI Desktop s interface, they will always refer to the values generated from the full-fit. All other forecast data displayed in the DI Desktop interface is from the best-efforts fit, since this fit represents most accurate trend using Value Navigator s auto-fit algorithm (Figure 3). Copyright 2015, Drillinginfo, Inc.

Figure 2: Full fit Figure 2: Best-efforts fit. The visuals above show the same production stream with both fits applied and how they affect the values of the decline parameters.

Oil and Gas Forecasts If an entity produces both oil and gas, they are not forecasted separately. The methodology used for forecasting oil vs. gas is as follows: 1. Calculate gas-to-oil ratio (GOR) using the last 12 month gas cumulative divided last 12 month oil cumulative. 2. This value is calculated every time an entity s production history changes. Recalculating GOR each time new production is reported allows the forecast to adjust as GOR changes through time. 3. Identify the primary product/phase using gas-to-oil ratio (GOR): a. If GOR > 20,000 CF/BBL, then the primary product is gas. b. Otherwise, the primary product is oil. 4. Calculate the primary product s forecast, also known as Remaining Recoverable Reserves (RRR). Then apply GOR to the RRR to estimate the non-primary product s forecast: a. If Primary Product = Gas: i. Gas EUR = Cumulative Gas + Gas RRR ii. Oil EUR = Cumulative Oil + (1/GOR * Gas RRR) b. If Primary Product = Oil: i. Oil EUR = Cumulative Oil + Oil RRR ii. Gas EUR = Cumulative Gas + (GOR * Oil RRR) Forecast Settings The Value Navigator auto-fit calculator has the ability to set different parameters before a stream of production data is analyzed and forecasted. The definitions below explain which parameters Drillinginfo has set and the values we provide to the auto-fit calculator. Currently, all entities use the same values for each of these parameters: Technical Termination Rate (Qlimit) The rate at which the product/phase becomes uneconomic. When a forecast hits this rate, it stops (Figure 4). Oil = 1 Bbl/day Gas = 25 MCF/day Economic Time Limit (Max Life) = 50 years. This value determines the maximum year to which the forecast created by the fit calculation will run (Figure 5). Exponential Transition = 0.05 (nominal). Once the slope of a harmonic or hyperbolic decline reaches this value, then the decline will transition into an exponential decline (Figure 5). Minimum Slope = 0.03. When a forecast s slope drops below minimum slope value, this triggers the need for additional segments to be added to the forecast. An inclining segment will be allowed to continue based on the Incline Period Duration setting. This will be followed Copyright 2015, Drillinginfo, Inc.

Figure 4: Technical termination rate shown for gas. by a flat segment based on the Flat Period Duration setting. The final segment will be a decline based on a fit from the historical data. Inclination Period Duration = 0.25. This controls the amount of time that a segment will be allowed to produce if it was fit with an initial nominal slope less than Minimum Slope. This value is entered as a factor of the duration of the incline trend in the historical data. For example, a value of 0.5 would be 50% of the incline trend. When the initial nominal slope is zero, the Incline Period Duration will be added to the Flat Period Duration and then set to zero. Flat Period Duration = 0.25. This controls the amount of time that a flat segment will be allowed to produce if it was created because of the Minimum Slope option. This value is entered as a factor of the duration of the incline trend in the historical data. For example, a value of 0.5 would be 50% of the incline trend. When the initial nominal slope is positive (shallow decline), the Flat Period Duration will be added to the Incline Period Duration and

Figure 5: Exponential transition and economic time limit. then set to zero. Hyperbolic B-Factor Bounds: Min = -0.5; Max = 4.0. The upper and lower bounds on the exponent that will be allowed on a hyperbolic fit. If the best fit s B-factor is outside of these bounds, the forecast is aborted. Additional Information Producing entities must have 6+ months of production in order to be eligible for a forecast. To determine if an entity is active for forecasting, each entity s last date of production is compared to the latest date where a full month of production is reported by the state agency (we refer to this date as the data date ). If the last production date is less than the data date minus 3 months (12 months for Oklahoma due to delays in OTC production reporting), the entity is considered inactive. An inactive entity s EUR is set to the cumulative production, which is a proven recoverable, and all other forecast columns are set to NULL. It is done this Copyright 2015, Drillinginfo, Inc.

way to assist aggregation over large areas. For example, if you are grouping by Operator, the EUR values in your selection are summed. If you are forecasting inactive wells, your summed EUR for all entities in your selection will be higher than actual since you are forecasting entities which could have continued to produce, even though they are now inactive. Entities which Value Navigator cannot forecast have their EUR set to the cumulative. Inclining declines Value Navigator can handle inclining production forecasts. The incline is allowed to continue for a duration of the incline, then it goes flat for a duration of the incline, then the decline begins using a fit from the historical data. See Minimum Slope, Inclination Period Duration, and Flat Period Duration in the Forecast Settings section. Historical forecast parameter values are not presented in DI Desktop. Only the delta between the current and last EURs are presented. PROACTIVE EFFICIENT COMPETITIVE By monitoring the market, Drillinginfo continuously delivers innovative oil & gas solutions that enable our customers to sustain a competitive advantage in any environment. Drillinginfo customers constantly perform above the rest because they are able to be more efficient and more proactive than the competition. PROACTIVE Success in Any Environment EFFICIENT Do More with Less COMPETITIVE Identify Opportunities Faster

Forecast Parameter Definitions As mentioned previously, not all parameters listed are functional within the DI Desktop interface. However, when a more detailed analysis is required, the following data is available for export to Excel. In the section titled Best-Efforts vs. Full Fits, we illustrate the difference between these two methodologies. Below, the data s column name is labeled appropriately to reflect if the data is from a best or full fit. Best-efforts fit column names begin BE_ while full fit column names begin with FULL_. When grouping is applied within DI Desktop, the forecast parameters undergo a mathematical operation to properly reflect the aggregation of data. Where applicable, each definition below contains a Default Aggregation Method, which will reflect the column s default method of aggregation (i.e. summed or averaged) when grouping occurs. EUR_PRIMARY_PRODUCT: The primary product (OIL or GAS) determined by the forecast process based on gas to oil ratio (GOR). Best-Efforts Fit Forecast Parameters: EUR_CALC_DATE: The date DI Desktop s forecast system processed this entity. BE_OIL_EUR: Best efforts oil Estimated Ultimate Recovery. Default aggregation method = sum. BE_GAS_EUR: Best efforts gas Estimated Ultimate Recovery. Default aggregation method = sum. BE_OIL_DELTA_EUR: The difference (in barrels - BBLS) between the last two oil EURs calculated by the DI Desktop forecast system for the best-efforts fit. BE_OIL_DELTA_EUR_PCT: The difference (as a percentage) between the last two oil EURs calculated by the DI Desktop forecast system for the best-efforts fit. BE_GAS_DELTA_EUR: The difference (in MCF) between the last two gas EURs calculated by the DI Desktop forecast system for the best-efforts fit. BE_GAS_DELTA_EUR_PCT: The difference (as a percentage) between the last two gas EURs calculated by the DI Desktop forecast system for the best-efforts fit. BE_B_FACTOR: This is the B-factor, or hyperbolic exponent (also referred to as Ni) for the best- efforts trend, not the full production history. The B-factor for the full production history is in FULL_B_FACTOR. BE_INITIAL_RATE: (Qi) Initial rate (BBL or MCF) of the primary product for the best-efforts fit. Note this is the rate at the beginning of the best-efforts trend, which is not necessarily the same as the rate at time zero (FIRST_LIQ or FIRST_GAS at FIRST_PROD_DATE). BE_FINAL_RATE: (Qf) Final rate (BBL or MCF) at termination date of primary product for the bestefforts fit. BE_END_DATE: Forecast s termination date of primary product for the best-efforts fit. Copyright 2015, Drillinginfo, Inc.

BE_INITIAL_DECLINE: (Di) Initial decline of the primary product for the best-efforts fit. Units are in effective secant. This is the initial decline of the best-efforts trend, which is not necessarily the same as the rate at time zero. The initial decline at time zero is in FULL_INITIAL_DECLINE. BE_OIL_RRR: Remaining/forecasted oil for the best efforts fit. BE_GAS_RRR: Remaining/forecasted gas for the best efforts fit. Full-Efforts Fit Forecast Parameters: FULL_OIL_EUR: Full fit oil Estimated Ultimate Recovery. FULL_GAS_EUR: Full fit gas Estimated Ultimate Recovery. FULL_B_FACTOR: The B-factor for the entity, or hyperbolic exponent (also referred to as Ni). This is the B-factor for the full-fit trend (i.e. the B-factor which represents the entire production history). Default aggregation method = average. FULL_FINAL_RATE: (Qf) Final rate (BBL or MCF) at termination date of primary product for the fullfit. Initial rate for the entire production history is in the column named FIRST_LIQ or FIRST_GAS. FULL_END_DATE: Forecast s termination date of the primary product for the full-fit. FULL_INITIAL_DECLINE: (Di) Initial decline of the primary product for the full-fit. Units are in effective secant. Default aggregation method = average. FULL_OIL_RRR: Remaining/forecasted oil for the full-fit. FULL_GAS_RRR: Remaining/forecasted gas for the full-fit. RG_DI Desktop EUR_Q315-03-Final; 07/21/15