Oil Production Greenhouse Gas Emissions Estimator (OPGEE ... · • GOR depends on API gravity, gas...

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Oil Production Greenhouse Gas Emissions Estimator (OPGEE): Model updates & changes Hassan M. El-Houjeiri Adam R. Brandt Department of Energy Resources Engineering Stanford University July 12 th , 2012

Transcript of Oil Production Greenhouse Gas Emissions Estimator (OPGEE ... · • GOR depends on API gravity, gas...

Page 1: Oil Production Greenhouse Gas Emissions Estimator (OPGEE ... · • GOR depends on API gravity, gas gravity, and temperature and pressure of crude oil • As reservoir pressure drops,

Oil Production Greenhouse Gas Emissions Estimator (OPGEE): Model updates & changes

Hassan M. El-Houjeiri Adam R. Brandt Department of Energy Resources Engineering Stanford University July 12th, 2012

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Overview

1.  Introduction 2.  Work to date 3.  Defaults

4.  Review & changes 5.  Documentation 6.  Moving forward

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1. Introduction

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Overview of OPGEE

• Oil Production Greenhouse Gas Emissions Estimator - Draft version A (OPGEE v1.0a) released for comments

• An open-source, fully public LCA tool for the estimation of GHG emissions from oil production operations

• Engineering-based bottom-up modeling of production, processing, storage and transport

4"

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OPGEE modeling goals

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Improve crude oil GHG modeling in 5 ways: 1.  Build a rigorous, engineering-based model of GHG

emissions from oil production operations 2.  Use disaggregated data for accuracy and flexibility 3.  Use public data where possible 4.  Document sources for all equations, parameters,

assumptions 5.  Maintain model as free to access, use, and modify

by any interested party

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2. Work to date

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OPGEE timeline

November 15th 2011 Scoping plan released March 16th 2012 OPGEE beta version released March 19th 2012 Public workshop July 3rd 2012 OPGEE Draft version 1.0a

released July 10th 2012 Draft CI summary tables and

input data released July 12th 2012 Public workshop

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Work since March 19th workshop

•  Defaults •  Use global averages where possible •  Smart defaults that interact with user inputs

•  Response to internal and external review •  Updates to model •  Debugging of model •  Improve user controls (avoid inconsistent inputs)

•  Generation of OPGEE Pro v1.0 •  More comprehensive front sheet •  Bulk assessment capability: running the model on

> 50 fields at a time and documenting inputs/results

•  Writing of OPGEE documentation

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3. Defaults

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Default - field age (Documentation, p. 45)

•  Data for 6502 global oil fields collected from Oil & Gas Journal 2010 Worldwide Oil Field Production Survey

•  4837 fields reported discovery dates

•  Global average field age (by count) is approx. 35 years (assuming 3 years development timeline)

•  Giant fields over 100 kbbl/d in 2000 (116 fields) have older age of ~40 years (production-weighted average)

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El-Houjeiri and Brandt Draft OPGEE v1.0 Documentation 47

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Figure 3.5: Distributions of giant oilfield ages. Mean date of discovery (by production-weighted average) is 1960.2.

3.3.3.4 Default gas composition

The default gas composition for associated gas from oil production is derivedfrom reported gas composition data from 135 California oil fields [3]. Speciesconcentration distributions for major gas species is shown in Figure 3.9. In or-der to remove outliers, all compositions with methane concentration less than50% were removed from the dataset (17 data points removed out of 135). The User Inputs

& Results3.3.2resulting mean compositions were rounded and used in OPGEE for default

gas composition.

3.3.3.5 Smart default for GOR

The gas-oil ratio (GOR) varies over the life of the field. The amount of gas ableto be evolved from crude oil depends on its API gravity, the gas gravity, andthe temperature and pressure of the crude oil [96, p. 297]. As the reservoirpressure drops, increasing amounts of gas evolve from the liquid hydrocar-bons (beginning at the bubble point pressure if the oil is initially undersatu-rated) [96]. This tends to result in increasing producing GOR over time. Also,lighter crude oils tend to have a higher GOR.

Because of this complexity, a static single value for GOR is not desirable.However, all data required to use empirical correlations for GOR is not likelyto be available for all crude oils modeled. Therefore we use California produc- User Inputs

& Results3.4.1ing GORs to generate average GORs for three crude oil bins.

El-Houjeiri and Brandt Draft OPGEE v1.0 Documentation 46

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Figure 3.4: Distributions of global oilfield ages. Mean date of discovery (by count notby production-weighted average) is 1978.4.

3.3.3.3 Default production per well

Country-level oil production data and numbers of producing wells were col-lected for a large number of oil producing countries. Data from a total of 107 oilproducing countries were collected from the Oil & Gas Journal 2010 WorldwideOil Field Production Survey [95]. Production data and operating well counts for2008 were collected from 92 of these 107 countries.

The distribution of per-well productivities for all countries is shown in Fig-ure 3.7. A majority of oil producing countries produced less than 500 bbl/well-d. Weighting these well productivities by country-level share of global produc-tion, we see a very similar distribution.

Because of the large number of countries producing less than 500 bbl/well-d, we plot the distribution for countries under 500 bbl/well-d (see Figure 3.8).For the 55 countries with per-well productivity less than 500 bbl/well-d, themost common productivity by number of countries was the 0-25 bbl/well-d.However, when weighted by total production, the most common productivitybin is 75-100 bbl/well-d.

In 2008, the world produced 72822 kbbl/d from 883691 wells, for an aver-age per-well productivity of 82 bbl/well. However, the very low productiv- User Inputs

& Results3.2.6ity of the US oil industry (representing ⇡512000 wells) pulls down this aver-

age significantly. Non-US producers averaged a per-well productivity of 183bbl/well-d, which is used as default well productivity in OPGEE.

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Default - field depth •  Data from Oil & Gas Journal 2010 Worldwide Oil Field Production

Survey

•  4489 fields with depth data

•  Mean depth is 7240 ft (by count)

•  The distribution is not normal. The median is shallower than the mean (6800 ft)

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El-Houjeiri and Brandt Draft OPGEE v1.0 Documentation 48

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Figure 3.6: Distributions of global oilfield depths in bins of 500 ft depth. N = 4489fields, mean = 7238 ft, SD = 3591 ft, median = 6807 ft.

Crude oils are binned by API gravity into heavy (< 20 �API), medium (�20, < 30 �API), and light crude (� 30 �API). Each California oil field is assignedan average API gravity using the following procedure:

1. API gravity by pool is collected from DOGGR datasets [97–99] and digi-tized.

2. If a range of API gravities is given for a single pool, the high and lowvalue are averaged to obtain a single value per pool.

3. The above steps give a set of single API values by pool. Each field has be-tween 1 and 17 pools that have data in DOGGR field properties datasets.

4. Each field is assigned an average API gravity using the following method:a) if a single pool API value is given for the field, that is used; b) if mul-tiple pool API gravities are given, and production data are available bypool, the pools are weighted by production level; c) if multiple pool APIgravities are given but no relative production data exist to weight thepools, the API gravities are averaged.

5. The above procedure results in a single average API gravity for eachfield in California.

The associated gas GOR for 174 California oil fields was compiled for Jan-uary to December 2010 [100, 101]. Five of these fields had very high GORs

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Default - production per well •  Data for production and producing wells from 107 countries (O&GJ 2010

Worldwide Oil Field Production Survey) •  Majority of countries produced less than 500 bbl/well-d •  Global average productivity is 82 bbl/well-d •  US stripper wells reduce average significantly •  Non-US per-well productivity = 183 bbl/well-d (OPGEE default)

a) All producing countries b) Countries producing less than 500 bbl/well-d

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El-Houjeiri and Brandt Draft OPGEE v1.0 Documentation 49

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Count!Fraction!

Figure 3.7: Distributions of oilfield per-well productivity (bbl oil/well-d) for bins of500 bbl/d, counted by numbers of countries (bar) and by fraction of production (dot)N = 92 countries.

Table 3.5: GOR values by crude oil API gravity bin.

Crude bin Num.fields

Gravity range Avg.gravity

MeanGOR

MedianGOR

[#] [�API] [�API] [scf/bbl] [scf/bbl]

Heavy 53 < 20 15.6 361 105Medium 65 � 20, < 30 25.0 843 594Light 51 � 30 35.4 1431 959

of above 10,000 scf/bbl and were removed as outliers, leaving 169 fields withdata. These data are binned as above based on their average API gravity value.The distributions, mean, and median values for each crude bin were generated(see Figure 3.10 for plot of distributions and Table 3.5 for listing of mean andmedian GORs by bin).

The median GORs are used to assign a smart default for each bin.

3.3.3.6 Default water oil ratio (WOR)

A smart default for the water oil ratio as a function of field age was gener-ated using data from hundreds of oil pools/fields in Alberta and California.Appendix D gives a thorough methodological explanation of the analysis un- User Inputs

& Results3.4.2derlying the WOR smart default.

El-Houjeiri and Brandt Draft OPGEE v1.0 Documentation 50

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Figure 3.8: Distributions of oilfield per-well productivity (bbl oil/well-d) for all coun-tries with per-well productivities lower than 500 bbl/well-d, counted by numbers ofcountries (bar) and by fraction of production (dot) N = 55 countries.

Table 3.6: OPGEE WOR relationships.

Case b0 b1 Source

Low 2.486 0.032 CA MeanOPGEE Default 1.75 0.05 User spec.High 1.168 0.091 AB mean

The default WOR is represented by an exponential function:

WOR(t) = aWOR exp[bWOR(t � t0)] [bbl water

bbl oil] (3.25)

where aWOR = fitting constant for the initial WOR in time = t0 [bbl water/bbloil]; bWOR = exponential growth rate [1/y]; t0 = initial year of analysis [y]; andt = year being modeled (independent variable) [y].

The results of fitting this model to the mean initial WOR and WOR growthrates in Alberta and California are shown in Figure 3.11, compared to oil fieldsfrom a variety of world regions. The tabular results for b0 and b1 for the Cali-fornia, Alberta, and default OPGEE cases are shown in Table 3.6.

3.3.3.7 Default waterflooding volume

The volume of water injected in a waterflooding project is meant to maintainreservoir pressure. As a default value, OPGEE assumes that the surface vol- User Inputs

& Results3.4.3

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Default - WOR (smart)

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WOR(t)=a ⋅exp[b(t - t0 )]

•  A smart default for WOR as a function of field age was generated using data from California and other regions (100s of fields)

•  WOR modeled using exponential function

•  Default case is a moderate case slightly higher than CA average

•  Appendix D of documentation shows analysis in detail

a = Initial WOR

b = WOR growth rate

DRAFT – For Discussion Only

July 10, 2012 5

Chart 1: WOR Smart Default (Replacement for the WOR Smart Default discussed in model documentation) In the absence of field-specific data on water-to-oil ratio (WOR), an estimate of WOR was made using the field start date. The chart below shows the default relationship. Some known values of WOR are plotted as a comparison to the default relationship.

Note: WOR relationship is changed from documentation due to removal of pool-level WOR data from Alberta

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Default - GOR (smart) •  GOR depends on API gravity, gas gravity, and temperature and pressure of

crude oil

•  As reservoir pressure drops, results in higher producing GOR over time. Lighter crude oil tends to have higher GOR

•  Crude oil are binned by API gravity into three categories: heavy (< 20 API), medium (>=20, ,30 API), and light crude (>= 30 API)

•  GOR for 169 California oil fields compiled for 2010 (binned by API gravity).

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Mean GORs are used to assign a smart default for each bin

El-Houjeiri and Brandt Draft OPGEE v1.0 Documentation 52

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umbe

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Gas oil ratio (scf/bbl)!

Heavy (<= 20 API)! Medium (>20, <=30 API)! Light (> 30 API)!

Figure 3.10: Distributions of California GORs, binned by crude density.

0!

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CA - Mean!AB - Mean!Default!ANS!CA Onshore!UK!Wyoming!Brazil!CA Offshore!Alberta!

Figure 3.11: Exponential WOR model using mean results for Alberta and Californiacase cases. Default case is a moderate case that is between the Alberta and Californiacases.

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Default - gas composition •  Default gas composition for associated gas is derived from gas

composition data from 135 California oil fields •  17 data points of methane <50 % removed

•  The mean compositions were rounded and used in OPGEE for default gas composition

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El-Houjeiri and Brandt Draft OPGEE v1.0 Documentation 51

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)!Nitrogen! CO2! Methane! Ethane! Propane!

Figure 3.9: Distributions of major gas species across 135 samples from California as-sociated gas producers.

ume is replaced, such that the total oil produced plus the water produced isreinjected, or the injection per bbl = 1 + WOR.

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Default - SOR

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El-Houjeiri and Brandt Draft OPGEE v1.0 Documentation 92

0.0000#

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#of#C

A#+#AB##

thermal#oil#and#bitumen

#produ

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#

Steam#oil#ra2o#(SOR,#bbl#water/bbl#oil)#

Figure 4.5: Distribution of SOR values for California and Alberta thermal EOR projects(steamflood, cyclic steam stimulation, steam-assisted gravity drainage).

•  Average steam oil ratios computed for California and Alberta thermal recovery projects (2009 and 2010 data)

•  Steam oil ratios binned by SOR and weighted by relative production volume

•  Mean SOR: •  CA: 3.3-3.4

•  AB: 3.3-3.6

•  Default = 3 bbl/bbl

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4. Review & changes

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• Thorough review of model •  Detailed review by ARB staff (J. Duffy) •  Review by ICCT (Galarza and Malins) •  Review by Energy-Redefined LLC (Howarth)

• Model debugging and testing •  Functionality testing of model during generation of

CA baseline •  Model run on >300 fields of widely varying

characteristics •  Allows us to understand power and limitations of

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Review and testing

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Updates & corrections: Gas balance •  Associated gas balance changed to properly account for

venting, fugitives and flaring •  Gas is now balanced by component •  User guidance ensures gas composition is consistent with

gas loss •  Flaring is assumed occurring before processing (allowing

early field production or production in locations where there is no gas market)

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Updates & corrections: VFF emissions

•  Flaring data table (Table 1) includes all oil producing countries [NOAA, 2010; EIA, 2010]

•  Estimating venting of CO2 from AGR unit by assuming venting of all CO2 remaining in the gas stream after other venting and fugitives

•  Estimating methane emissions from AGR unit on volume basis rather than on unit basis using data from EPA on methane emissions from AGR unit [EPA, 1996]

•  Gas recovered is properly accounted for, re-enters gas balance

EPA. Methane Emissions from the NG Industry. Technical Report, Environmental Protection Agency, 1996

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Updates & corrections: production energy consumption

•  Corrected gas compressor work equation (found by both internal and external review).

•  Lifting pressure calculations adjusted account for the pressure drawdown between the reservoir and the bottomhole.

•  Added optional methane flooding to account for cases when the gas injected is more than the amount of gas produced (found by both internal and external review).

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Updates & corrections: surface processing and water treatment"

•  Corrected calculation of electricity for makeup water treatment"

•  Recalculated amine circulation rate based on the changes to the gas balance (CO2 venting from AGR unit and flaring before gas processing)"

•  Added NGL options "•  Percentage of ethane, butane and propane condensed in

the demethanizer"•  End use disposition of NGL (blend or export)"

•  Adjustments to defaults and user controls"

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Restructuring of gathering sheets"•  Changed front sheet calculations for readability. "•  Complete GHG emissions breakdown by component and type

of direct emission "•  Calculation of indirect emissions by fuel type (Table 1 & 2 of

‘GHG Emissions’ gathering sheet) "

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Updates & corrections: steam injection

• Steam injection fueled with produced crude added as option

• Gas turbine calculations performed using more rigorous methods

• Small errors and bugs fixed to ensure energy balance

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Addition: Bitumen extraction and upgrading"

•  Bitumen extraction and upgrading added from GHGenius model"•  Integrated & non-integrated mining & upgrading"•  In situ to diluted bitumen"

•  Primary inputs from GHGenius extracted"•  Energy inputs by pathway"•  Energy types breakdown"

•  Integrated with OPGEE emissions system"•  Consistent fuel characteristics ensures alignment

with rest of OPGEE results"•  Slight differences between OPGEE and

GHGenius results for same pathway"

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Updates & Changes: User interaction sheet •  Input parameters changed to ratios (e.g. gas flooding injection

volume to injection ratio) •  Added bitumen emissions and changed graphical output to

show bitumen emissions breakdown •  Added 0.5 g of CO2 emissions for miscellaneous emissions

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5. Documentation

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Documentation •  Explains model calculations and assumptions •  Provides information on model data sources and calculations of

defaults •  Designed to integrate well with model

1.  Goals and motivation for OPGEE are described

2.  Structure and usage of OPGEE is introduced

3.  Production stages are explained in detail, outlining the calculation methods and assumptions

4.  Supplemental calculation sheets are explained in detail (e.g. steam injection, VFF, etc.)

5.  Gathering sheets are explained

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6. Moving forward

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Moving forward

• Modeling is generally complete • Minor changes before next comment period

•  Tank emissions currently not included, will be included after revising tank calculations

•  Integrate field-level flaring results from UC Davis/NOAA

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Thank you"