AUTHOR ACCEPTED MANUSCRIPT FINAL PUBLICATION INFORMATION Terrorism, Geopolitics, and Oil Security Using Remote Sensing to Estimate Oil Production of the Islamic State The definitive version of the text was subsequently published in Energy Research and Social Science, (Forthcoming 2018), 2018-04-30 Published by Elsevier THE FINAL PUBLISHED VERSION OF THIS MANUSCRIPT IS AVAILABLE ON THE PUBLISHER’S PLATFORM This Author Accepted Manuscript is copyrighted by World Bank and published by Elsevier. It is posted here by agreement between them. Changes resulting from the publishing process—such as editing, corrections, structural formatting, and other quality control mechanisms—may not be reflected in this version of the text. You may download, copy, and distribute this Author Accepted Manuscript for noncommercial purposes. Your license is limited by the following restrictions: (1) You may use this Author Accepted Manuscript for noncommercial purposes only under a CC BY-NC-ND 3.0 IGO license http://creativecommons.org/licenses/by-nc-nd/3.0/igo. (2) The integrity of the work and identification of the author, copyright owner, and publisher must be preserved in any copy. (3) You must attribute this Author Accepted Manuscript in the following format: This is an Author Accepted Manuscript by Do, Quy-Toan; Shapiro, Jacob N.; Elvidge, Christopher D.; Abdel-Jelil, Mohamed; Ahn, Daniel P.; Baugh, Kimberly; Hansen-Lewis, Jamie; Zhizhin, Mikhail; Bazilian, Morgan D. Terrorism, Geopolitics, and Oil Security © World Bank, published in the Energy Research and Social Science(Forthcoming 2018) 2018-04-30 CC BY-NC-ND 3.0 IGO http://creativecommons.org/licenses/by-nc- nd/3.0/igo © 2018 World Bank Terrorism, geopolitics, and oil security: Using remote sensing to estimate oil production of the Islamic State a Quy-Toan Do b Jacob N. Shapiro c Christopher D. Elvidge d Mohamed Abdel-Jelil e Daniel P. Ahn f Kimberly Baugh g Jamie Hansen-Lewis f Mikhail Zhizhin h* Morgan D. Bazilian a Research Department, The World Bank, United States
b Politics Department, Princeton University, United States
c Earth Observation Group, NOAA National Centers for Environmental Information, United States
d Middle East and North Africa Chief Economist’s Office, The World Bank, United States
e Johns Hopkins University SAIS and Office of the Chief Economist, U.S. Department of State, United States f Cooperative Institute for Research in the Environmental Sciences, University of Colorado, United States
g Economics Department, Brown University, United States
h Payne Institute, Colorado School of Mines, United States. *Corresponding author. Email address: mbazilian@mines.edu. Abstract As the world’s most traded commodity, oil production is typically well monitored and analyzed. It also has established links to geopolitics, international relations, and security. Despite this attention, the illicit production, refining, and trade of oil and derivative products occur all over the world and provide significant revenues outside of the oversight and regulation of governments. A prominent manifestation of this phenomenon is how terrorist and insurgent organizations—including the Islamic State group, also known as ISIL/ISIS or Daesh—use oil as a revenue source. Understanding the spatial and temporal variation in production can help determine the scale of operations, technical capacity, and revenue streams. This information, in turn, can inform both security and reconstruction strategies. To this end, we use satellite multi- spectral imaging and ground-truth pre-war output data to effectively construct a real-time census of oil production in areas controlled by the ISIL terrorist group. More broadly, remotely measuring the activity of extractive industries in conflict-affected areas without reliable administrative data can support a broad range of public policy and decisions and military operations. 1. Introduction While oil resources play a crucial part in conflict, geopolitics, and international affairs [1], the exploitation of oil, like other resources, often occurs where governance is unstable and transparency is absent. Illicit production, refining, and distribution of oil and products occurs even in well-established markets, such as Nigeria [2]. Like other illicit trades, the scale of production is difficult to gauge and daunting for policymakers to ignore [3]. To improve the policy response to conflict in areas with illicit oil production, we adapt earlier methods of measuring oil production from satellite data. We tailor our approach to the setting of the Islamic State group, also known as ISIL/ISIS or Daesh, in Syria and Iraq. Measuring illicit oil production is important because natural resource extraction substantially affects conflicts. In such areas reliable external measures of forestry, mining, and oil production can enable better approaches to a broad range of challenges. In Colombia and Nigeria, for example, insurgent organizations have long controlled territory where oil is produced, and in many regions around the world public and reliable field-level production numbers are difficult to find. Estimating production remotely can enable governments and international organizations to identify illegal or untaxed production and to better understand its role in post-conflict economies as well as the impact of sanctions, trade restrictions, and other policy interventions. Previous work on the relationship between resource exploitation and conflict underscores the relevance of this study. Even though a main concern is that resource wealth can fund armed groups either directly or via taxation, variation in the relationship occurs. For instance, Sánchez de la Sierra [4] finds that positive prices shocks to a bulky commodity leads armed groups to create a monopoly of violence to impose taxation and regulate production in Eastern Congo. Along the same lines, Maystadt et al. [5] use data on international mineral prices and historical mining concessions to show that armed groups tend to reduce violence in areas near the mines when prices go up. This “protection effect” is consistent with violence reducing economic profitability. Others find that fighting around diamond mines did not affect civilians in Sierra Leone, but was rather limited to violence among soldiers [6,7]. This result is echoed in Ziemke [8], who finds that violence against civilians was lower in diamond areas of Angola. Finally, Dube and Vargas [9] find that price shocks have heterogeneous effects: in labor-intensive sectors, commodity price drops result in higher incentives to join armed groups, while in the capital-intensive sector the rise in the price elicits predatory behavior from armed groups. This study also relates to previous work on the relationship between conflict and oil extraction in particular Colgan [10] considers the impacts of conflict and the oil sector primarily as it influences domestic policy formation. Relatedly, Månsson [11] develops a framework to consider the wider issues of energy and conflict. Further work on conflict and offshore oil [12], how these issues relate to renewable energy and concepts of energy independence [13,14], and how these issues manifest in Russian and the Ukraine [15] all appear in ERSS. This paper combines the focus on oil and conflict with the emerging literature that uses remote sensing to measure oil production. Our approach builds on previous work establishing globally how flared gas volumes from oil fields and refineries can be measured with nighttime light detecting sensors such as DMSP-OLS and SNPP-VIIIRS [16,17]. Remote sensing based 1 estimates of gas flaring have been validated at the production site [18] and country-year levels [19] and found to be accurate and unbiased. Additional work uses satellite observations to examine the behavior in extractive industries in low-governance regions [20,21]. We make several contributions to the literature. In the remote sensing literature, we use a new method combining visible and infrared bands to detect low level production with greater sensitivity. While previous methods failed to distinguish low intensity flares from other signals of conflict in our setting, our algorithm to detect and calibrate flaring with light of the visible band when the infrared signal is below noise level is novel and effective. Further, while earlier studies have documented a robust positive correlation between radiant heat as measured from satellite and flared gas volume at the production site, we quantitatively estimate a structural relationship between oil production and radiant heat, then use the estimates to predict production in places where radiant heat can be measured but production cannot. This is, to our knowledge, the first attempt to do so. In the oil and conflict literature, we demonstrate that using remote sensing can be an appropriate method to measure socio-economic activity in environment where direct observation is either infeasible or prohibitively expensive. Variants on the approach presented here could be applied in a much broader set of geographies and circumstances. Section 2 considers the context of Islamic State oil production. Section 3 presents our methodology, Section 4 provides results, and Section 5 concludes. 2. Background on Islamic State oil production The non-state insurgent organization known as the Islamic State group (also called the Islamic State [IS], the Islamic State of Iraq and the Levant [ISIL], the Islamic State of Iraq and al-Sham [ISIS], or Daesh, its Arabic acronym) took control of large swathes of territory in Syria and Iraq beginning in mid-2013 (Fig. 1). 1 Its rapid territorial expansion began when fighters from the Islamic State of Iraq (ISI) started operating in Syria in April 2013 and accelerated from early 2014 onwards when the group moved aggressively back into Iraq [22]. For a time, the group was considered the richest jihadist group in the world and was thought to raise money from a variety of sources [23]. In 2014 and 2015 revenue from oil production in areas the group controlled was often cited as its largest potential source of revenue flow, with estimates of weekly oil revenue ranging from “several million” to US$28 million [24,25]. Any reasonable assessment of the organization’s long-run survival prospects had to account for these revenues and identify how sustainable they were [26]. Yet no reliable sources existed at the time. Beginning in late of 2015, ISIL steadily lost territory in both Iraq and Syria, but still maintained substantial territory in both countries as of early 2017. Much was written in the media about the ISIL oil revenue stream, including its severe decline due to Coalition operations [27–49]. 2 Warrick [50] shows how satellite images can identify ISIL micro-refining (or “teapot”) capability, asserting that “[t] he proliferation of micro- refineries is the latest sign of strain in the group’s self-declared caliphate, which has lost half its 1 We henceforth use ISIL, Daesh, and Islamic State interchangeably. 2 For more recent coverage see, e.g., https://www.brookings.edu/on-the-record/how-isis-uses-oil-to-fund-terror/ or https://www.newyorker.com/news/news-desk/the-isis-oil-trade-from-the-ground-up. 2 territorial holdings in Iraq since late 2014.” Robinson et al. [51] offer a wider look at how remote sensing techniques can be employed to understand the ISIL economy. Several news reports, show the more recent strategy by the Trump Administration against ISIL oil [52]. Metrics of the effort include the amount of oil trucks across the supply chain from distribution to production and refining destroyed by Coalition forces. The numbers show a massive uptick in targets just in the first half of 2017, part of the roughly two year old Operation Tidal Wave II. Significant detail on the ways the failing ISIL is finding revenue are detailed in a recent article in the Financial Times, which includes starting their own currency [53]. Early accounts of the group’s oil production and the revenues generated indicated that oil was a significant source of financing for the organization. The 2014 Oil Market Report of the International Energy Agency estimates an output of 70,000 barrels per day (bpd) [54]. Other news outlets give numbers around 50–60,000 bpd yielding an income of US$2.5m per day [55] to more than US$3m per day [56]. Early estimates by the US Departments of State and Treasury put the organization’s oil revenues at around US$1m per day [57]. These estimates were then revised down to “a couple million dollars a week” after the U.S. started air-strikes against the organization’s assets [58]. Views as to whether ISIL was financing itself through oil, external support, extortion, or taxes then evolved, with higher emphasis put on taxes and extortion as primary sources of revenues over time. Die Zeit for instance reported December 2014 oil revenues to be a mere US$370,000 per day or even lower at US$260,000 [59]. An October 2015 article however gives an estimated output of 34–40,000 bpd, earning the organization an average of US$1.5m per day [60]. In sum, there was no consensus on the production numbers or revenue they created. 3. Methodology 3.1. Data We use data from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors deployed on the NOAA/NASA Suomi NPP satellite [17,61,62] to detect location of all oil flares over Syria and Iraq from March 2012 to November 2016. VIIRS data have moderate spatial resolution (∼1 sq km) and cover the globe every 24 h. We used a combination of manual infrastructure checks and algorithms to ensure all flaring sites were associated with oil production [63]. For each oil production site in the region we use news outlets’ reports, agencies’ press releases and Institute for the Study of War (ISW) maps allow us to assign individual oil wells to the Islamic State group’s control at the daily level. During our period of study, 42 production sites in both Syria and Iraq (34 in Syria and 8 in Iraq; see map on Fig. 1) had been or are under ISIL control, out of a total of 75 identified oil sites in Syria and 114 in Iraq. Finally, we combine the satellite data with data on oil production at ISIL and nearby oil fields obtained from IHS Energy Intelligence and Wood Mackenzie Refs. [64–66]. Production data included field name, location, and annual oil production in barrels per day from 2012 to 2015 for 22 significant oil fields in Syria and Iraq comparable to those under ISIL control. 3.2. Calibration Our approach relies on the property that the extraction of oil is associated with the liberation of natural gas, primarily methane, which is initially dissolved in crude oil in constant 3 proportions. In the region of study, the default mode of operation is to flare this gas, hence generating radiation that is captured by VIIRS sensors. To determine the proportion of oil to gas flared (RH), we estimated the relationship between the log of annual mean RH and log of annual mean barrels oil produced per day from the production data of 48 field-year observations. Consistent with previous work [16,17], we found a robust relationship between radiant heat and oil output that closely approximated previous estimates with modest and unbiased noise. Provided the oil-RH relationship estimated using data prior to the seizure of oil fields by ISIL still holds thereafter, we can make consistent statistical inference about contemporaneous volume of crude oil extracted from contemporaneous measures of RH. 3.3. Venting Oil extraction can take place without gas flaring, a process called venting. Although there was no evidence of venting the sample of study, we assign historic output values in cases without a sensor detection to account for this possibility. At any given moment, a site is in one of three production states: S1: The site is producing with natural gas flaring. Oil production with gas flares is the standard production technique in the sample. No regulatory impediments curtailed gas flaring in Syria and Iraq before the oil fields were contested [67]. 
 S2: The site is producing without gas flaring (i.e., venting). A site that extracts crude oil could simply vent the accumulated natural gas. Venting would happen on a site if: (i) the operator voluntarily shuts down the pilot flame in the flare stack; or (ii) long periods of inactivity extinguish the pilot, which is not re-ignited as production resumes. 
 S3: The site is inactive. A site might become inactive if fighting has damaged productive infrastructure or no qualified personnel are available to operate the site. Distinguishing between these production states is important since the lack of radiant heat (RH) does not imply an absence of oil production: venting remains a possibility. Because the narrow spectral bands used in the estimation of RH—the near-infrared (NIR) bands M7 and M8, the short-wave-infrared (SWIR) band M10, and the mid-wave-infrared (MWI) bands M12 and M13—are not sensitive enough to detect low light from low-intensity flaring, we also make use of data from VIIRS’ Day/Night band (DNB). DNB is a wide, visible, and NIR imaging spectral band designed to detect moonlit clouds. The DNB’s low detection limits make it possible to detect electric lighting present at the Earth’s surface which cannot be sensed in the infrared spectral channels [68–71]. DNB is even sensitive to moonlight reflection on the Earth. DNB detection, once the effect of the lunar cycle has been accounted for [62], therefore allows discriminating between states S2 and S3 when no infrared signals are detected. Fig. 2 illustrates how the RH, temperature, and DNB trends correspond to production states and major events at Ajil oil field in Iraq. From 2012 through 2013, consistently high RH (top panel) and temperature (middle panel) indicate production activity was normal (S1). When ISIL takes over in June 2014 through January 2015, flaring intensity drops substantially and temperature detections become sporadic; yet, DNB measures (bottom panel) remain substantial and are mostly greater than would be possible with electrical lighting. This pattern indicates a 4 mix of activity with flare (S1) and activity without flare (S2). Consistent with this interpretation, an engineer at the site told Reuters in July 2014 that ISIL fighters were pumping low volumes of oil from the field [72]. Finally, from January to March 2015 there is no activity at the site (S3) since only the lunar cycle lighting is detected (bottom row). After March 2015, we observe fires from fighting as ISIL flee the site and a period of low-level DNB radiance before regular activity resumes. 3.4. Validation As a validation exercise, we compare our estimates with the best available intelligence of ISIL oil production [73]. On 15 May 2015, U.S. Army Special Forces killed a senior ISIL leader known as Abu Sayyaf, also known as Fathi al-Tunisi, the alleged head of the ISIL oil company. According to the U.S. Department of Defense, Abu Sayyaf, “helped direct the terrorist organization’s illicit oil, gas and financial operations.” This raid yielded significant amounts of intelligence on the ISIL economy, including administrative data providing ISIL oil production at the governorate-month level for September 2014–January 2015. We aggregate to monthly totals to avoid error in interpreting administrative divisions. 4. Results Fig. 3 reports our production estimates with 95% confidence intervals from January 2014 through November 2016. We find ISIL oil production peaked at roughly 86,000 barrels per day (bpd) in late July 2014 and declined steadily thereafter, averaging approximately 56,000 bpd in the second half of 2014. Production dropped through most of 2015 until rising briefly in late 2015 to 52,000 bpd. In these figures, we assume that fields without a radiant heat signal could be venting when the DNB detection is above the lunar illumination threshold (S2) and assign historical production on venting days. The estimates are slightly lower without this substitution [63]. The confidence intervals account for uncertainty in the calibration of radiant heat to barrels of oil. As a benchmark, major oil producing countries produce on the scale of 500,000 (Ecuador) to 10,000,000 bpd (Saudi Arabia). We reject the conclusion that ISIL production at its peak was comparable to a major petrostate given the upper bound of the confidence interval. Several precautions ensure these estimates provide an upper bound on ISIL oil production. First, as the gas-to-oil ratio (GOR) gradually increases as oil is extracted, our inferences based on the assumption that the GOR remained constant at its pre-war level will overestimate output when converting RH to oil production. Second, our assumptions are intentionally biased towards over-estimating rather than under-estimating production. For example, the cutoff value we choose to separate electric lights from low-intensity flaring is set at a level that rules out electric lights with probability one, while infrared detection still happens at much lower levels of radiance. Last, we assign cloudy days the average of production at the site over the month. This method is generous since production is highly sporadic at some sites and even one day of production will be smoothed over the entire month. We performed a validation of this procedure where we randomly assigned clouds to 10% of the observations and obtained higher estimates. Our findings, while lower than some accounts provided in the media, are consistent with estimates for the early period of ISIL control that relied on captured documents [73]. In 5 particular, administrative data captured from the Abu Sayyaf raid posits daily production ranging from 52,120 bpd to 55,560 bpd in the time frame from roughly June to October 2014. These numbers are well within the bounds of our 95% confidence intervals and very close to our point estimates (Fig. 4). Despite the consistency, other figures available in the documents seized during the Abu Sayyaf raid suggest potential problems with ISIL’s administrative data. For example, the captured documents indicate significant output for the At Tayyanah field in the Al Khayr Governorate, for which satellite data detected no visual activity and which was understood to be non-active prior to ISIL takeover. Likewise, simple arithmetic inconsistencies reduce confidence in the accuracy of these records. It is also plausible that internal political motivations that may bias administrative data upwards. Overall, even if the Abu Sayyaf records contain some inaccuracies, they still provide a rough signal of the level of production that our results meaningfully replicate. The decline in oil production occurred as a result of both closing oil fields and lowered production at active oil fields. In total, we observe no activity during ISIL control for 68.5% of field-days and regular activity during ISIL control for 9.9% of field-days. This degree of low activity and no activity during ISIL control is irregular in comparison to the historical activity of the fields before ISIL took control. Fig. 5 displays the portion of sites observed with a flare (column 1), the mean RH at all sites (column 2), and the mean RH at active sites (column 3), a rough measure of production intensity, for ISIL and nearby non-ISIL oil fields over time. All measures are normalized with pre-war values (March – June 2012) in row 2 to highlight changes over time. We observe that fields ISIL would control at some time after June 2014 had substantially reduced flaring activity by mid-2013 both in terms of the share of days flaring and average radiant heat per active flare. We also observe additional reductions in flaring activity in late 2015. We interpret these patterns as indication that ISIL was unable to operate many wells they came to control. Since many fields were contested for years of war prior to ISIL control, it is highly plausible that the infrastructure was destroyed or improperly maintained prior to their arrival. A potential lack of personnel with the right skills could have compounded the difficulty of rebuilding and operating the sites. Territory losses in 2016 further contributed to the production fall. 5. Conclusions With satellite data, we estimate oil production in territory under ISIL’s control. Our results show that ISIL performed poorly in comparison to historical trends at extracting oil in the fields under its control. There are no reliable price data on which to gauge how this modest production quantity translated into income. Early reports, however, recount that ISIL was selling at a discounted price, ranging from $20 to $35 dollars in 2014 [74], while a subsequent report indicated that prices depend on the field of origin, and that some fields charged $40 to $45 dollars per barrel [75]. At such levels, even assuming no investments in maintenance, the annual revenue from oil would be far below many published estimates. Assuming a $30 per barrel average price and no investments in maintenance, net oil revenues in 2015 would have been roughly $380M, well short of the funds needed to compete on the battlefield against the combined forces arrayed against the group in that year. Moreover, the significant global oil price fall from 2014 to 2016 exacerbated the revenue fall when production declined in 2015 and 2016. One reason why our estimates differ from many publicly-available ones is small sample 6 size. To our knowledge, prior estimates relied on what is effectively a survey of ISIL’s oil assets. Information was obtained from a few selected sites and at specific dates on the basis of key documents or interviewee self-reports, which were then extrapolated. In spite of being supplemented with expert opinions, generalizations to the universe of ISIL-controlled oil facilities are intrinsically imprecise in that the underlying data have observations that are few and might not be representative, therefore leading to imprecise and potentially biased inferences. Updating these estimates over time faces similar methodological challenges. The approach proposed here instead conducts a real-time census of ISIL oil production facilities with daily temporal resolution. The estimates coming from our analysis are not inferences made from observations on a few selected sites and at a few selected dates but from all sites and in real time. Thus, they have the substantial advantage of enabling less bias than previous estimates of the impact of various kinds of events (e.g., attacks, leadership conflicts, territorial losses, etc.) on oil production. Our approach adds accuracy and transparency to the measurement of economic activity in poorly governed and conflict afflicted territories that is needed to improve the policy response. Reliable external measures of oil output in conflict afflicted settings can enable better approaches to a broad range of policy and military challenges. In this case, the main military response was Operation Tidal Wave II, based on Operation Tidal Wave 1, which targeted Nazi oil supplies in Romania during World War II. According to the US Department of Defense, the operation aims to “...target ISIL’s entire oil distribution chain, which includes trucks, wellheads, pumps and collection points” in part because oil funded an estimated 50% of ISIL operations [76]. Location- date level production developed with our method can ensure this policy response is accurate, timely, and targeted. More broadly, the approach can be adapted to inform planning for short- term humanitarian assistance and long-term reconstruction. Even though oil production is a major source of revenue, among the world’s poorly-governed states, few report reliable oil production numbers. In these settings, our method of measuring production can provide critical inputs for governments and international organizations to establish sound economic policy, identify illegal or untaxed production, and assess the impact of policy changes. Acknowledgements The authors are grateful to Jishnu Das, Shanta Devarajan, Fabrice Mosneron Dupin, Tarek Ghani, Guy Grossman, Martyn Howells, and Roy van der Weide for helpful comments. The views and opinions expressed are those of the authors and do not necessarily represent the views or official positions or policy of the U.S. Department of State, U.S. National Oceanic and Atmospheric Administration, the U.S. Department of Commerce, the U.S. Government, or the World Bank, its Board of Executive Directors, or the governments they represent. The institutions do not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of the institutions above concerning the legal status of any territory or the endorsement or acceptance of such boundaries. This paper is based on a World Bank Policy Working Paper [63], and funded by Empirical Studies of Conflict Project (ESOC) and the Development Research Group of the World Bank. 7 References [1] D. Yergin, The Prize, Simon and Schuster, 1990. [2] L. Hoffman, P. 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