Energy Intensity and Greenhouse Gas Emissions from Tight Oil Production in the Bakken Formation

Low-carbon production of iron and steel: Technology options, economic assessment, and policy

Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers

Effectively addressing today’s energy challenges requires advanced technologies along with policies that influence economic markets while advancing the public good.

Chichimene is an oil field located in the Colombian eastern basin. Since 2014, enhanced oil recovery (EOR) through the water injection process has been carried out successfully in part of this field. A polymer injection pilot was carried out over five injection patterns to optimize this process as a promising strategy for polymer-based EOR in challenging reservoir conditions with high temperatures and heavy oil. Polymer injection started in 2015 in the first injection pattern, and it was extended between 2018 and 2019 to five injector wells covering different areas of the oil field. 21.65 MSTB of polymer-augmented water were injected (approximately 7,000 t of polymer). At the end of 2022, the incremental oil production had reached around 4.9 MBbls (Million barrels of oil), and it is expected to increase up to 6.9 MBbls, resulting in a 9.84% incremental oil recovery factor by the end of the water displacement period. Considerable incremental oil recovery concerning the pilot patterns’ production baseline and notable carbon and energy intensity improvements were observed. Life cycle GHG (Green House Gas) emissions and energy consumptions associated with the crude oil production chain were estimated. Moreover, a suitable calculation tool of carbon footprint and energy intensity related to each stage of crude oil recovery by polymer injection processes was performed. The main contributions of GHG emission and energy consumption for the polymer-based EOR process analyzed were linked to lifting, oil dehydration, water clarification treatment, and Injection systems. The contribution of each stage in the total carbon footprint of crude oil production was calculated for the polymer flooding and compared with oil baseline production. The carbon footprint of implementing the oil recovery project was calculated for eight years. The results show that the carbon intensity of the crude oil baseline production in the five patterns is close to 20 kg CO2-eq/BO (barrels of oil) on average. Nevertheless, EOR by polymer injection decreases to 19.3 kg CO2-eq/BO. Regarding the energy intensity, the oil baseline production of the five patterns achieved 8.5 kWh/BO; however, the specific energy consumption in the EOR process by polymer injection decreased to 6.7 kWh/BO. The reduction is attributed mainly to energy use optimization in crude oil lifting and enhanced water re-injection with polymer. Furthermore, the carbon intensity reduction could lead to more than 3,200 t of CO2-eq avoided and improve the energy performance of polymer-based EOR by more than 21% concerning the baseline case. Nevertheless, a significant potential GHG emissions reduction of more than 4,000 t CO2-eq could be expected for the whole impact of this pilot project in the Chichimene field.

This paper introduces GHGfrack, an open-source engineering-based model that estimates energy consumption and associated GHG emissions from drilling and hydraulic fracturing operations. We describe verification and calibration of GHGfrack against field data for energy and fuel consumption. We run GHGfrack using data from 6927 wells in Eagle Ford and 4431 wells in Bakken oil fields. The average estimated energy consumption in Eagle Ford wells using lateral hole diameters of 8 (3)/4 and 6 (1)/8 in. are 2.25 and 2.73 TJ/well, respectively. The average estimated energy consumption in Bakken wells using hole diameters of 6 in. for horizontal section is 2.16 TJ/well. We estimate average greenhouse gas (GHG) emissions of 419 and 510 tonne of equivalent CO2 per well (tonne of CO2 eq/well) for the two aforementioned assumed geometries in Eagle Ford, respectively, and 417 tonne of CO2 eq/well for the case of Bakken. These estimates are limited only to GHG emissions from combustion of diesel fuel to supply energy only for rotation of drill string, drilling mud circulation, and fracturing pumps. Sensitivity analysis of the model shows that the top three key variables in driving energy intensity in drilling are the lateral hole diameter, drill pipe internal diameter, and mud flow rate. In hydraulic fracturing, the top three are lateral casing diameter, fracturing fluid volume, and length of the lateral.

Modeling of energy consumption and related GHG (greenhouse gas) intensity and emissions in Europe using general regression neural networks

Germany strives to reduce its greenhouse gas (GHG) emissions by at least 80% by 2050 compared to 1990 levels. In the underlying roadmap (Climate Action Plan 2050), natural gas is declared to be a bridge energy carrier into a carbon neutral era. But, since the roadmap only describes guiding principles and abstract transition paths, the concrete role of natural gas remains unclear. By analyzing 36 scenarios of eleven recent studies with respect to their GHG emissions, natural gas consumption, as well as their transition paths, we aim at understanding how decarbonization intensity and decarbonization strategies influence German future natural gas consumption. We find that as long as GHG reductions are less than 70% in comparison to 1990 levels natural gas consumption remains almost constant on average. However, German natural gas consumption in the scenarios varies by up to 500 TWh/a for identical GHG emission reductions. This divergence is driven by the decarbonization strategies applied. While some scenarios focus on switches from oil and coal to natural gas to mitigate GHG emissions, others favor a combination of energy efficiency and electrification of appliances currently running with natural gas. When GHG emission reductions are intensified, natural gas consumption declines considerably in almost all considered scenarios and the variances of projected natural gas consumptions narrow. In order to achieve those GHG emission levels, switches to natural gas are no longer sufficient. Hence, natural gas needs to be replaced. This is especially true when GHG emissions should be reduced by more than 80% compared to 1990 levels. Applied decarbonization strategies vary from extensive electrification of the end-use sectors, to a production of huge amounts of renewables (fuels and electricity), to an extensive utilization of synthetic fuels. All of these decarbonization methods reduce natural gas consumption. Only the scenario allowing for carbon capture and storage or usage in a large scale paves the way for constant natural gas consumption levels while still achieving high GHG abatement goals.

Cities are expected to play a major role in managing climate change in the coming decades. The actual environmental performance of urban centres is difficult to predict due to the complex interplay of technologies and infrastructure with social, economic, and political factors. Machine learning (ML) techniques can be used to detect patterns in high-level city data to determine factors that influence favourable climate performance. In this work, rough set-based ML (RSML) is used to identify such patterns in the Sustainable Cities Index (SCI), which ranks 100 of the world’s major urban centres based on three broad criteria that cover social, environmental, and economic dimensions. These main criteria are further broken down into 18 detailed criteria that are used to calculate the aggregate SCI scores of the listed cities. Two of the environmental criteria measure energy intensity and greenhouse gas (GHG) emissions. RSML is used to generate interpretable rule-based (if/then) models that predict energy utilisation and GHG emissions performance of cities based on the other criteria in the database. Attribute reduction techniques are used to identify a set of 7 non-redundant criteria for energy use and 9 non-redundant criteria for GHG emissions; 6 criteria are common to these two sets. Then, RSML is used to generate rule-based models. A 10-rule model is determined for energy intensity, while an 11-rule model is found for GHG emissions. Both models were reduced further by eliminating rules with weak generalisation capability. A key insight from the rule-based models is that social, environmental, and economic attributes are associated with energy intensity and GHG emissions due to indirect effects.

TotalEnergies 2030 objective is to achieve a net 40% reduction in scope 1 and 2 greenhouse gases (GHG) emissions compared to 2015. For ADNOC, Carbon management, electrification, energy efficiency and methane reduction are at the core of the strategy to achieve a 25% reduction in GHG intensity by 2030 and be operationally net zero by 2045. CO2 EOR has the potential to significantly reduce, and potentially fully decarbonize ADNOC operations, especially in onshore oil fields. ADNOC onshore shareholders have the ambition to reach an annual CO2 storage volume of 10 million tons per annum (MTPA) through EOR by 2030. CO2 can be collected from anthropogenic sources or from oil fields associated gas and injected into oil reservoirs not only to enhance the oil recovery, but also to sequestrate the CO2within the oil reservoirs. Estimating the EOR related GHG emissions accurately includes calculating GHG emissions related to the CO2 recycling operations with the additional oil production treatment, and the amount of stored CO2 after recycling. The present publication introduces how ForCFR, an internally developed and innovative GHG forecaster (SPE-221930-MS) is used to perform EOR GHG emissions calculation, which can then be used to obtain the net carbon intensity of EOR technologies application. ForCFR is integrating energy consumption and GHG emissions evaluation in production forecasting tools (reservoir modeling softwares). The application of EOR GHG incremental emissions has been done on a preliminary WAG CO2 project case including the calculation of incremental energy consumption, incremental GHG emissions, CO2 storage, and, consequently, net oil carbon intensity (negative) and stored CO2 (positive). In this paper, EOR WAG CO2 results are discussed in detail and a case on enhancing CO2 storage in the aquifer below is also presented. This additional storage benefits from the facilities developed by the WAG CO2 to enhance CO2 storage at much lower cost than for a standalone CCS (Carbon Capture and Storage) project. The joint operation of WAG CO2 and CCS at the same location creates many synergies for operation, monitoring and ensuring the integrity of the CO2 storage. The results obtained prove that EOR technologies can contribute to the reduction of the overall carbon intensity of oil production. For the EOR CO2, the stored CO2 is not only compensating for the full scope 1 & 2 emissions linked to the EOR project, but also reducing by 30% the carbon intensity of the incremental oil production itself (scope 1, 2 & 3). Thanks to the synergies between CO2 CCS and EOR CO2, the additional storage is multiplying by more than two the volume of CO2 stored, which is then compensating around 40% of the incremental oil production Scope 3 GHG emissions. In addition, the CO2 storage cost is minimized.

ABSTRACT: Horizontal well drilling and multistage hydraulic fracturing have been widely applied in the Bakken tight oil reservoir to enhance its recovery. However, the primary oil recovery was low due to the majority of oil being remained in the rock matrix. Gas huff-n-puff (HnP), as an enhanced oil recovery (EOR) method, has been applied to the Bakken wells. However, fracture interference among wells leads to fast pressure decline during the gas injection period and slow recovery during the production period. By designing strategies that combine water injection in the offset wells with gas HnP, the pressure of the gas HnP well can be maintained during the production period. Therefore, the effectiveness of gas HnP can potentially be improved. A five-well reservoir model was built using a third-party reservoir simulator with an embedded discrete fracture modeling method. The simulated production data obtained from the reservoir model was successfully matched with the field data. Multiple natural gas HnP strategies were designed and simulated to compare the EOR performances. The results showed that a larger volume of the injected natural gas contributes to more incremental oil production. Higher water injection pressure provided better pressure containment for the gas HnP well, but high water cut presented a detrimental effect on oil production. 1. INTRODUCTION The Bakken Formation has witnessed a boost in oil production since 2008. By the end of March 2022, the oil production of the Bakken region was approximately 1.2 million barrels per day (U.S. Energy Information Administration, 2022). Although the Bakken formation is tight with ultra-low reservoir permeability, the applications of horizontal well drilling and multi-stage hydraulic fracturing have contributed to the success of Bakken. A typical Bakken well has 50 stages of hydraulic fractures in the 10,000 ft lateral section. These hydraulic fractures serve as the "channels" that connect the well with the tight Bakken reservoir, thus the oil that was originally locked in the formation could easily flow to the wellbore. The interaction between hydraulic fractures and pre-existing natural fractures also contributes to larger stimulated reservoir volume and promotes incremental oil production (Aoun et al., 2022; Kolawole and Ispas, 2020; Wan et al., 2020a). Currently, the new Bakken wells have an average initial oil production of 1963 barrels per day (U.S. Energy Information Administration, 2022). With the current oil price of $115 per barrel, a new well could generate an average revenue of $225,745 per day. Despite a large amount of oil that has been produced, the oil recovery of the Bakken Formation remains close to 10% (Hoffman and Evans, 2016). Alternative enhanced oil recovery (EOR) strategies are needed to fully recover the shale oil locked in the tight reservoir matrix.

A rapid increase in horizontal drilling and hydraulic fracturing in shale and “tight” formations that began around 2000 has resulted in record increases in oil and natural gas production in the U.S. This study examines energy consumption and greenhouse gas (GHG) emissions from crude oil and natural gas produced from ∼8,200 wells in the Eagle Ford Shale in southern Texas from 2009 to 2013. Our system boundary includes processes from primary exploration wells to the refinery entrance gate (henceforth well-to-refinery or WTR). The Eagle Ford includes four distinct production zones—black oil (BO), volatile oil (VO), condensate (C), and dry gas (G) zones—with average monthly gas-to-liquids ratios (thousand cubic feet per barrel—Mcf/bbl) varying from 0.91 in the BO zone to 13.9 in the G zone. Total energy consumed in drilling, extracting, processing, and operating an Eagle Ford well is ∼1.5% of the energy content of the produced crude and gas in the BO and VO zones, compared with 2.2% in the C and G zones. On a...

An estimation of energy and GHG emission intensity caused by energy consumption in Korea: An energy IO approach

Major US electric utility climate pledges have the potential to collectively reduce power sector emissions by one-third

Trajectory, driving forces, and mitigation potential of energy-related greenhouse gas (GHG) emissions in China's primary aluminum industry

Canada is one of the most emission-intensive economies in the world and the big challenge for Canada and its provinces is in how to mitigate the GHGs while keeping the same pace of economic growth. This paper’s main objective is to examine the relationship between greenhouse gas (GHGs) emissions and clean growth using cross-sectional data for Canadian provinces from 1995 to 2019. Based on the results of the cross-sectional dependence, slope heterogeneity, and Hausman test, the study applied the pooled mean group (PMG) estimators. For the robustness of the results, the study also provided the results of augmented mean group (AMG) estimators. The results of Westerlund’s test show that the variables of the estimated models are cointegrated in the long run except in the case of the carbon intensity (GHGs/Energy) model, while no short-run relationship was observed. The main findings of both estimators show that an inverted U-shaped relationship exists in the case of the carbon intensity model. In contrast, as expected, a U-shaped relationship exists in the case of the energy intensity model. The results also confirmed that Canada reduced its GHGs emissions after 2005 and that GHGs emissions and energy intensity are decreasing over time. At the province level, only Alberta has no long-run relationship as regards carbon intensity and energy intensity, while Nova Scotia and British Colombia have no long-run relationship as regards energy intensity. In terms of tipping points, Canada is in the increasing phase of the inverted U-shaped curve in the case of carbon intensity, while in the decreasing phase of the U-shaped curve in the case of energy intensity. There is a significant decrease in greenhouse gas emissions per capita at the provincial level compared to the 2005 base levels. It is imperative to reduce greenhouse gas emissions per capita in Canada and its provinces over time by gradually rolling out energy-saving incentives rather than by using more efficient energy-saving technology. The government of Canada should shift towards low-carbon energy and renewable sources which emit fewer greenhouse gases per unit of energy produced.