Abstract This paper estimates changes in the energy return on investment (EROI) for five large petroleum fields over time using the Oil Production Greenhouse Gas Emissions Estimator (OPGEE). The modeled fields include Cantarell (Mexico), Forties (U.K.), Midway-Sunset (U.S.), Prudhoe Bay (U.S.), and Wilmington (U.S.). Data on field properties and production/processing parameters were obtained from a combination of government and technical literature sources. Key areas of uncertainty include details of the oil and gas surface processing schemes. We aim to explore how long-term trends in depletion at major petroleum fields change the effective energetic productivity of petroleum extraction. Four EROI ratios are estimated for each field as follows: The net energy ratio (NER) and external energy ratio (EER) are calculated, each using two measures of energy outputs, (1) oil-only and (2) all energy outputs. In all cases, engineering estimates of inputs are used rather than expenditure-based estimates (including off-site indirect energy use and embodied energy). All fields display significant declines in NER over the modeling period driven by a combination of (1) reduced petroleum production and (2) increased energy expenditures on recovery methods such as the injection of water, steam, or gas. The fields studied had NER reductions ranging from 46% to 88% over the modeling periods (accounting for all energy outputs). The reasons for declines in EROI differ by field. Midway-Sunset experienced a 5-fold increase in steam injected per barrel of oil produced. In contrast, Prudhoe Bay has experienced nearly a 30-fold increase in amount of gas processed and reinjected per unit of oil produced. In contrast, EER estimates are subject to greater variability and uncertainty due to the relatively small magnitude of external energy investments in most cases.

Citation: Tripathi VS, Brandt AR (2017) Estimating decades-long trends in petroleum field energy return on investment (EROI) with an engineering-based model. PLoS ONE 12(2): e0171083. https://doi.org/10.1371/journal.pone.0171083 Editor: Vanesa Magar, Centro de Investigacion Cientifica y de Educacion Superior de Ensenada Division de Fisica Aplicada, MEXICO Received: September 7, 2016; Accepted: January 16, 2017; Published: February 8, 2017 This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Data Availability: All relevant data are within the paper and its Supporting Information files. Funding: VST was supported by Stanford University faculty support funds to ARB during the writing of this paper. Competing interests: The authors have declared that no competing interests exist.

Introduction This paper is adapted from the M.S. thesis of Tripathi for publication in PLOS ONE [1]. Energy return on investment Monetary flows shape the behavior of individuals and countries. This behavior includes the evaluation of energy resources, which are typically judged using the measures of monetary returns. However, monetary accounting has been criticized for providing an incomplete assessment of energy resource quality. The measurement of energy flows associated with an energy resource was posed as an alternate quality assessment framework by Odum [2]. Odum argued that energetic metrics offer a more accurate, physics-based evaluation of a primary energy resource’s true utility [2]. Within this framework, Hall et al. defined energy return on investment (EROI) as the ratio of energy production to the required energy inputs associated with producing a primary energy resource [3]. EROI has been estimated using a variety of methods and definitions for many types of energy resources, including petroleum fields. Murphy, et al. provide a method for defining the EROI boundary consisting of two variables: (1) the boundary at which energetic returns are measured, and (2) the boundary at which energetic investments are estimated [4]. Their method includes a proposed “standard” EROI and their paper summarizes the details of EROI estimation [4]. In this typology, ratios with boundary “1” include only extraction of energy sources, while ratios with boundary “2” also include refining or processing. Murphy et al. also classify EROIs by inclusion of only direct inputs “d”, or including both direct and indirect inputs “i”. EROI 1,i serves as the standard EROI within the Murphy et al. system [4]. Several recent studies have estimated the EROI of various petroleum resources over time. An example is the analysis of the Canadian petroleum industry by Poisson and Hall [5]. They use data from the Canadian government on the direct energy consumption of the Canadian petroleum sector to estimate the energy investment used in calculating EROI 1,d [5]. They estimate the Canadian petroleum sector’s combined direct and indirect energy consumption as the product of the sector’s energy intensity factor [units energy/units currency] and the financial value of the sector’s hydrocarbon production. They estimate that Canadian petroleum production EROI stnd declined by 13% during the 1990-2008 period [5]. Another temporal EROI analysis focuses on the Russian petroleum sector [6]. Nogovitsyn and Sokolov use direct energy consumption reports to estimate EROI for the overall Russian petroleum industry and for two major Russian natural gas producing companies, Gazprom and Novatek [6]. Nogovitsyn and Sokolov estimate that the NER dev. and transp. (similar to EROI 1,d ) of the overall Russian petroleum sector decreased by 17% during the 2005-2012 period [6]. Hu et al. estimate several EROI ratios for China’s Daqing field, including EROI 1,d and EROI stnd , using energy and financial expenditures flowing into Daqing and Chinese industrial energy intensity factors [7]. During 2001-2009 they estimate that Daqing’s EROI 1,d declined by 22% and its EROI stnd declined by 35%. Daqing’s EROI decline profiles were fairly smooth over the 2001-2009 period [7]. In another recent work, a model based on engineering principles is used to estimate a current EROI for forty petroleum fields [8]. Brandt et al. obtain data on field properties and extraction practices. The engineering-based model then estimates the energy investments required to perform these petroleum field operations. Brandt et al. estimate two types of EROI: a net energy return (NER) and an external energy return (EER). While this NER is noted as comparable to EROI stnd , their model did not include embodied material inputs. Brandt et al. found great variation in the estimated EROI for the various fields; factors such as higher intensity of enhanced oil recovery operations resulted in fields with relatively lower EROIs [8]. An earlier temporal analysis of onshore oil fields in California, U.S. used a simpler model also based on engineering principles [9]. Brandt estimates that during the 1955-2005 period the NER of California oil fields declined by approximately 92% (starting at 63 and ending at 5, approximately) [9]. When crude refining is added, NER declined by only 44% during 1955-2005 due to a lower initial EROI value. Temporal EROI analysis has also been applied to unconventional hydrocarbon resources [10]. Brandt, Englander et al. analyze the direct energy consumption input and output flows of the Alberta, Canada bitumen industry to estimate several NER and EER ratios during 1970-2010 [10]. Their “mine mouth” and “point of use” NERs are similar to EROI 1,d and EROI 2,d , respectively, of Murphy et al. but do not include embodied energy inputs [4, 10]. They estimate that mine-mouth NER values of Alberta bitumen production were generally stable during 1970-2010, remaining around 5. Notably, their estimated “mine mouth” EER values for bitumen produced using mining methods are significantly higher and more variable because processed bitumen is used to power a significant portion of oil sands. The methodology of this work is similar to that of Brandt et al [8], but here we shift the focus toward a deep temporal analysis of a relatively small (but diverse) set of very large petroleum fields over decades. The temporal field-level focus of this paper is similar to the scope of Hu et al. [7]. Additionally, this analysis considers indirect consumption of energy embodied in the manufacturing of petroleum field materials, wells, and equipment.

Materials and methods Introduction to methods Five petroleum fields were selected for analysis: Wilmington and Midway-Sunset in the U.S. (California), Cantarell in Mexico, Forties in the U.K., and Prudhoe Bay in the U.S. (Alaska). The objective is to track the EROI of large fields over a long period of time: a quarter-century or longer, if possible. It is expected that estimated EROI values will decline for all petroleum fields, but the precise decline profiles are unknown. All of the selected fields are “giants” with at least 2.9 billion barrels of estimated ultimately recoverable reserves (URR). This study focuses on giant fields because, while relatively few in number, they account for a large share of global petroleum production [11]. The fields selected represent a range of reservoir parameters and production practices. The fields include onshore and offshore fields and fields with heavy and light oils. The reservoirs vary with regard to key factors such as depth and water-oil-ratio. These reservoir parameters in turn affect post-primary recovery production practices. Introduction to the oil production greenhouse gas emissions estimator (OPGEE) model The EROI of each petroleum field is estimated over time using the Oil Production Greenhouse Gas Emissions Estimator (OPGEE) [12]. OPGEE v2.0a was used with minor modifications to drilling energy estimates. OPGEE calculates greenhouse gas emissions based on the energy consumption of a petroleum field’s production operations [13]. OPGEE can therefore be used to model the energy invested into a field. Energy contained within the oil and within any exports of natural gas, natural gas liquids (NGLs), and electricity is the basis for calculating the energy returns from a petroleum field. OPGEE receives parameters regarding the functioning of a petroleum field. These include the choice of production processes such gas lift, basic reservoir parameters such as average pressure, and choices regarding the processing of crude oil and natural gas. When input data is not available, OPGEE applies or calculates default values based on the literature [14]. OPGEE then uses engineering principles and technical data to estimate the energy requirements of the major production steps. These calculations are divided into processing stages. The stages considered in this analysis are Embodied Emissions, Drilling, Production & Extraction, Surface Processing, and Crude Transport. Table 1 summarizes the energy sources used in the processes associated with each OPGEE upstream stage. El-Houjeiri, Brandt, and Duffy provide an overview of OPGEE’s structure and example calculations [13]. Brandt documents OPGEE’s estimation of the energy embodied in petroleum field equipment, facilities, and materials [15]. PPT PowerPoint slide

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larger image TIFF original image Download: Table 1. The energy sources used in the processes associated with each OPGEE upstream stage. Data from Brandt et al. [8]. https://doi.org/10.1371/journal.pone.0171083.t001 Using OPGEE to estimate EROI Estimation of EROI is performed by calculating the net energy ratio (NER) and external energy ratio (EER) for each field according to the general procedure from Brandt et al. [8]. Fig 1 is a schematic of the OPGEE processing stages used in this analysis and accompanying energy investments, energy returns, and energy waste flows, adapted from Brandt et al. [8]. Subscripts 1-5 correspond to the OPGEE process stage associated with a given flow. PPT PowerPoint slide

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larger image TIFF original image Download: Fig 1. Schematic of OPGEE production processes and related energy flows used to calculate NER and EER values. Figure adapted from Brandt et al. [8]. https://doi.org/10.1371/journal.pone.0171083.g001 The energy flows are now defined, for process stage n and time-step t as: Fuel Cycle Energy Investments, F n,t External Energy Investments, E n,t Internal Energy Investments, I n,t External energy investments E n,t represent energy originating from outside the petroleum field imported to run operations. An example is electricity imported for water treatment processes. Fuel cycle energy investments F n,t represent the energy consumed to produce external energy investments. Internal energy investments I n,t consist of energy produced at the petroleum field that is used within the petroleum field, rather than being exported. An example is combustion of natural gas to operate acid gas removal (AGR) units. There are two categories of energy outflows proceeding from the processing stages: Wasted Energy Flows, W n,t Energy Returns, R n,t Wasted energy flows represent loss of associated gas from operational flaring, venting, or fugitive emissions. It is assumed here that waste energy flows emanate only from the Production & Extraction and Surface Processing stages. Energy returns represent the energy content of the hydrocarbons (oil, natural gas, or NGLs) leaving the system as well as exported electricity generated at the field. Instead of being exported, a portion of the produced natural gas is routed for use as internal energy flows (I 3 and I 4 ) that drive processes within Production & Extraction and Surface Processing. In the case of the early Midway-Sunset and Wilmington modeling periods, I 3 also contains crude oil combusted to generate steam. All five OPGEE process stages also receive external energy investments E n,t (diesel, electricity or residual fuel). Using these energy investment and returns flows, two types of EROI ratios are calculated: the net energy return (NER) and the external energy return (EER). Additionally, NER and EER are each calculated in two ways: (1) by including only the energy returns from oil (NER oil and EER oil ), and (2) by including all energy returns (NER total and EER total ). Thus, a total of four EROI ratios are calculated for each field. Following Brandt et al. the ratios are conceptually defined in Eqs (1)–(4) as [8]: (1) (2) (3) (4) The system boundaries of the energy investments used to calculate NER are similar—but not equivalent—to those of EROI stnd in the method of Murphy et al. [4]. This analysis considers the energy requirements of extraction at the five fields. It also considers the energy required to prepare and transport crude oil for refining, but the energy required to refine the oil is excluded. The energy requirements of processing and transportation of produced natural gas are also included. Because the EROI stnd of Murphy et al. includes only the energy requirements of extraction, our system boundary is more inclusive along this dimension [4]. This analysis considers direct and indirect energy inputs. However, all embodied energy requirements, which are a component of indirect energy inputs, are not included. OPGEE does not include the energy costs of producing consumed materials such as chemicals used for natural gas processing or new metals used during well maintenance. It is also assumed that capital investment in processing equipment such as compressors occurs only once. Energy requirements for construction of the drilling machinery and other field structures are also not considered in OPGEE. Our system boundary is thus less inclusive along this dimension of the EROI stnd of Murphy et al. [4]. Henceforth the term “modeling period” is used to denote the years during which each is field is analyzed. For example, 1974-1999 is the Forties field modeling period. OPGEE’s Energy Consumption sheet contains summary calculations of energy flows [12]. Energy investment flows F n,t , E n,t , and I n,t are aggregated within these summary tables. These summary tables and other OPGEE model cells are used to calculate NER and EER as follows: Table 2 contains references to the OPGEE sheet and cell numbers to calculate energy energy investment and returns flows, based partially on the method of Brandt et al. [8]. The following abbreviations are used for OPGEE sheet names: EC = Energy Consumption, FS = Fuel Specs, R = Results, AF = Active Field. For example, “AF J63” refers to cell J63 within the Active Field sheet. PPT PowerPoint slide

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larger image TIFF original image Download: Table 2. Calculation of NER and EER flows from OPGEE model cells. Calculations based partially on the method of Brandt et al. [8]. https://doi.org/10.1371/journal.pone.0171083.t002 The flows calculated in Table 2 are used to calculate NER and EER values in Eqs (5)–(8): (5) (6) (7) (8) Note that energy returns from natural gas, R gas,t , are calculated slightly differently than energy returns from NGLs, R ngl,t , and energy returns from electricity, R elec,t . R gas,t contains the energy content of both the exported natural gas and the fuel cycle costs that would have been associated with its production, had the gas been produced elsewhere from the modeled petroleum field. In contrast, R ngl,t and R elec,t contain only the energy content of the exported NGLs or electricity, respectively. OPGEE’s treatment of the energy required for drilling was modified for this analysis. OPGEE’s standard treatment uses an “expected lifetime well productivity” factor [bbl oil/well drilled], based on analysis of approximately one century of drilling and oil production statistics in California. This factor is used to calculate a drilling energy intensity factor [MMBtu consumed during drilling/MMBtu oil produced] approximated over the lifetime of a field [14]. To focus instead on the energy consumption of drilling for a particular year—rather than energy consumption distributed over the life a field—OPGEE was modified to calculate the drilling energy intensity factor for each year including only the wells drilled in that year (see [1] for more details).

Sensitivity analysis Using OPGEE to estimate a field’s EROI is an approximate process involving the generalization of locally heterogeneous and uncertain reservoir parameters to field-level assessments. Furthermore, details regarding the surface processing of crude oil are not generally available. As an example sensitivity analysis variable, consider OPGEE’s use of the productivity index, which directly affects energy requirements for downhole pumping [12]. OPGEE’s default injectivity ratio—which affects water injection energy requirements—is also set equal to the productivity ratio [12]. Downhole pumps and water injection are production practices modeled in Forties, Wilmington, Midway-Sunset, and Prudhoe Bay. For these fields sensitivity analysis was performed by using productivity ratios of 1 bbl/psi-d and 50 bbl/psi-d. Sensitivity analysis was performed for each field for three time increments of the modeling period: early (the first three years), middle (the middle three or four years), and late (the final three years). The NER or EER for each time increment is calculated as the average of the values over each year of the increment. Table 6 contains a summary of the sensitivity analysis settings. PPT PowerPoint slide

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larger image TIFF original image Download: Table 6. Summary of sensitivity analysis parameters (C = Cantarell, F = Forties, MS = Midway-Sunset, PB = Prudhoe Bay, W = Wilmington). https://doi.org/10.1371/journal.pone.0171083.t006

Conclusion All five fields analyzed in this study exhibit significant declines in NER/EROI over time. The temporal declines in EROI estimates observed in this study result both from decreasing oil and gas production and increasing energy investments required for processing and handling fluids. NER values declined significantly for all fields. EER estimates were more complex. NER and EER estimates have notable differences: NER values include internal energy flows and thus more completely capture the energy requirements of secondary and tertiary production methods. Compared to NER values, EER values are subject to much larger uncertainty stemming from small modeling changes. Poor data availability regarding the oil and gas processing scheme was a significant source of uncertainty regarding NER estimates, particularly earlier in the modeling periods. Improved estimates of reservoir pressure would reduce modeling uncertainty, especially in the case of Wilmington where water injection is a major energetic cost. In the future OPGEE may be improved by considering NGL exports when calculating the energetic cost of petroleum transport. OPGEE’s calculation of the embodied energetic cost of injection wells in cases without water injection could also be improved. The results of this analysis suggest further opportunities for temporal EROI estimates of oil and gas fields. Other fields with similar reservoir properties and production processes may be analyzed. For example, Midway-Sunset is a heavy oil field for which steam generation is the primary energy investment. How does its temporal EROI profile compare to those of similar fields? Kern River and South Belridge are two other large California fields with heavy oil and extensive histories of steam injection. CA-DOGGR records are comprehensive and readily allow for expansion of this analysis to these additional fields. Further analysis of steam injection-dominated fields may allow for the ultimate generation of “EROI temporal type curves” for particular combinations of reservoirs and production parameters, such as “heavy oil/steam injection.” Also, as discussed above, many temporal EROI analyses use an economic approach of applying energy intensity factors to financial expenditures. The Daqing field study by Hu et al. is an example [7]. Using OPGEE to estimate Daqing’s EROI could allow for a preliminary comparison of the two approaches. Importantly, OPGEE’s calculations of the indirect energy costs embodied in the construction of petroleum field materials should be expanded to include additional categories such as drilling machinery.

Supporting information S1 File. Supporting information file S1_File.xlsx contains additional input data and results in numerical form. https://doi.org/10.1371/journal.pone.0171083.s001 (XLSX)

Author Contributions Conceptualization: VST ARB. Data curation: VST. Formal analysis: VST. Funding acquisition: ARB. Investigation: VST. Methodology: VST ARB. Project administration: ARB. Software: VST ARB. Supervision: ARB. Validation: VST. Visualization: VST. Writing – original draft: VST. Writing – review & editing: VST ARB.