Report S3
Crude Oil Movement Estimates for 1997
Table of Contents
1. Definition of the Commodity Flow Data Gap
1.1. General Description
The general transportation logistics of the petroleum industry include the initial gathering of crude oil in production fields for domestic sources and from marine terminals for foreign imports. Crude petroleum is delivered to refineries or to long-term storage facilities such as the Strategic Petroleum Reserve. From these refineries, finished products are moved to markets throughout the nation.
Transportation of crude petroleum is accomplished by a variety of land- and marine-based modes. They include: pipeline, railroad tanker cars, tanker trucks, barges, and oceangoing tankers. On a volume basis, pipelines and marine vessels are predominately used in transporting petroleum, but trucks also play an essential function.
The Commodity Flow Survey (CFS) does not cover oil and gas extraction industries within NAICS code 211; thus, crude petroleum is out-of-scope.
1.2. Commodities Involved in the Data Gap
1.2.1. SCTG codes
Crude Petroleum is defined under the two-digit SCTG code 16 - Crude Petroleum Oil. This is an out-of-scope commodity for the 1997 CFS.
1.2.2. STCC codes
Under STCC coding, crude petroleum is included in “13 - Crude Petroleum, Natural Gas, or Gasoline.”
1.3. Establishments Involved in the Data Gap
1.3.1. NAICS codes
211 - Oil and Gas Extraction
486 - Pipeline Transportation
Both NAICS categories were excluded from the CFS 1997 and 2002 sampling frames. This is an out-of-scope data gap for CFS.
1.3.2. NAICS-SIC conversion issues
There are no major NAICS-SIC conversion issues for this data gap because crude petroleum is out-of-scope for the CFS. Crude petroleum has been a problem area for the CFS caused by reasons unrelated to establishment definitions (i.e. NAICS verses SIC). The NAICS-SIC conversion does affect, to a certain extent, some data on hazardous materials such as fuel oil, gasoline, and propane that are transported by the highway mode.
2. Importance of the Data Gap
2.1. Value and Tonnage as a Share of National Shipments
Table 1 contains a national summary of Crude Oil movements estimates described in this report. Our estimates show that about 5.4 billion barrels of Crude Oil, equal to 744 million tons, were delivered by pipeline in 1997.1 National Waterborne Commerce data from the Corp of Engineers show Crude Oil movements on the waterway at about 117 million short tons in 1997. Crude Oil movement by truck was derived from Table 46 of the Energy Information Administration (EIA) Petroleum Supply Annual for 1996 and 1998, since this table was not available for 1997. In total, we estimate about 872 million tons (or 6.4 billion barrels) of Crude Oil movement in 1997.
 |
Tons (Thousands) |
Ton-Miles (Millions) |
|---|---|---|
| Pipeline | 743,945 |
223,184 |
| Rail | 2,385 |
477 |
| Waterborne | 117,026 |
143,190 |
| Truck | 8,904 |
1,513 |
| Total | 872,261 |
368,363 |
2.2. Value and Tonnage as a Share for Individual Modes
Based on ton-miles Table 1 shows that pipelines carry approximately 61%, while waterways carry approximately 39% of total Crude Oil movements. Motor carriers and railways account for the rest.
2.3. Geographic Concentration: Dispersed versus Concentrated, Local versus Long Distance
Crude petroleum shipments are transported long-distance via pipeline to various locations in the United States. According to the Pipeline Economics, Oil & Gas Journal, the average pipeline shipment length is about 230 miles. This study found the average distance for pipeline shipments to be 300 miles and the average distance for all transport modes to be 422 miles.
2.4. Importance to International Trade
Imported Crude Oil is a large part of the total crude processed in the 146 operating refineries across 35 states. On average, 8,200 thousand barrels per day of crude petroleum was imported during 1997. A very small amount of crude petroleum (107 thousand barrels per day) was exported from the United States in 1997. Domestic Crude Oil production averaged about 6,450 thousand barrels a day during 1997. About than 60 percent of U.S. refinery inputs were from imported crude petroleum.
3. Data Sources
3.1. Coverage in CFS
CFS does not include SCTG code “16 – Crude Petroleum Oil.”
3.2. Coverage in Other Data Sources
Basic information on crude production, imports, exports, and disposition at refineries is collected by EIA within the U.S. Department of Energy (DOE). EIA’s information on crude petroleum imports and exports at ports can be supplemented by the Annual Imports and Exports Waterborne Databanks, prepared and maintained by the Maritime Administration Office of Statistical and Economic Analysis in the U.S. Department of Transportation (DOT). Several other data sources were also consulted in deriving estimates for 1997, including:
- Oil Pipelines: Annual Report (Form 6) of oil pipeline companies provided to the Federal Energy Regulatory Commission.
- Water Carriers: Waterborne Commerce of the United States, U.S. Army Corps of Engineers, Part 5, Table 2-2.
- Motor Carriers: Petroleum Tank Truck Carriers Annual Report, American Trucking Association, Inc. and Petroleum Supply Annual, U.S. DOE, EIA, Volume 1, Table 46.
- Railroads: Carload Waybill Statistics, Report TD-1, USDOT, Federal Railroad Administration and Freight Commodity Statistics, Association of American Railroads, Table A3.
Tables 2 and 3 below show inter-PADD estimates of Crude Oil movements by modes in 1997 based on EIA data.2
| From | To |
|||||
|---|---|---|---|---|---|---|
|
PADD 1 | PADD 2 | PADD 3 | PADD 4 | PADD 5 | Total |
| PADD 1 | 0 | 0 | 5,256 | 0 | 0 | 5,256 |
| PADD 2 | 846 | 0 | 12,228 | 9,587 | 0 | 22,661 |
| PADD 3 | 0 | 721,106 | 0 | 0 | 0 | 721,106 |
| PADD 4 | 0 | 34,781 | 10,351 | 0 | 0 | 45,132 |
| PADD 5 | 0 | 0 | 35,535 | 0 | 0 | 35,535 |
| Total | 846 | 755,887 | 63,370 | 9,587 | 0 | 829,690 |
| From | To |
|||||
|---|---|---|---|---|---|---|
|
PADD 1 | PADD 2 | PADD 3 | PADD 4 | PADD 5 | Total |
| PADD 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| PADD 2 | 1,457 | 0 | 0 | 0 | 0 | 1,457 |
| PADD 3 | 0 | 0 | 0 | 0 | 0 | 0 |
| PADD 4 | 0 | 0 | 0 | 0 | 0 | 0 |
| PADD 5 | 0 | 0 | 0 | 0 | 0 | 0 |
| Total | 1,457 | 0 | 0 | 0 | 0 | 1,457 |
3.3. Data Quality
Data sources listed above are all government published statistics. They are expected to be reliable.
4. Estimation Methods
4.1. General Description of Estimation Method
Crude Oil information collected by EIA provides a starting point for estimating Crude Oil movements as required by the FAF. The origins of crude petroleum include domestic production by state and imports. Imports data for Crude Oil identify port cities and states, as well as state of the processing refinery. The approach to estimating Crude Oil movements for 1997 is slightly different from that used for the 2002 FAF. This is because a comprehensive database on pipeline movements was not available for 1997 as was the case for 2002. We estimated Crude Oil movements at the regional level and then sum up to derive the national estimates shown in Table 1.
4.2. Method for Estimating Regional Flows
The approach employed for deriving regional estimates of Crude Oil movements is a combination of bottom-up and top-down approaches. Data on Crude Oil movements were collected from publicly available sources. Waterborne data was available at the state-level, and rail movements were estimated from the Rail Waybill database at the state level. Unlike our 2002 estimates, there was no comprehensive database on pipeline movement of Crude Oil for 1997. Truck movement data was only available at the national level from the EIA and AOPL. We derived a comprehensive estimate of water, rail, and pipeline movement at the state level, and then disaggregated the estimates to FAF regions.
4.2.1. Rail
Crude Oil movement by rail was derived directly from the Waybills database by multiplying each sample shipment by its expansion factor and summarizing to the state level. To calculate ton-miles, we used an average miles per ton of about 1,200 miles.
4.2.2. Waterway
Waterborne Commerce data is an enumeration of state-to-state Crude Oil movements. An interesting check of this data is to compare the inter-PADD movement by tanker and barge from the EIA to the inter-PADD aggregation of the waterborne data for Crude Oil. As shown in Table 3 the EIA data show movements of 1.5 million barrels from PADD 2 and 1. An aggregation of the Waterborne Commerce data on the other hand shows no records of inter-PADD movements. This discrepancy is small when compared to the about 850 million barrels of waterborne Crude Oil movements in 1997. We note that the national number given in the Waterborne Commerce database is about 25 percent greater than that computed by aggregating the state data. Accordingly we proportionally increased the state-to-state data to derive the estimates shown in this report. For ton-miles, an average distance per ton of 1,100 miles was calculated from the national data.
4.2.3. Pipeline
There is no comprehensive data from the FERC Form 6 database for 1997 because collection of this data in electronic form started in 2000. We adopted an approach where the pipeline estimates are derived as the net balance of Crude Oil at the origin states (domestic and imports) and destination states (refinery locations) and movements by rail and water. For this purpose we hold the state-to-state estimates for rail and water, as described above, fixed. We did not include truck movements in this calculation because truck movements are short-hauls. For domestic oil production we obtained the state-level oil production data from the EIA website. Company level records of imports which include the port state of importation and the destination state for processing was also obtained from the EIA website. The sum of domestic production and imports represents the total amount of Crude Oil originating from each state.
Given that imports data also identify imported Crude Oil processing states, we take these as destinations for the imports. In order to derive destinations for the remaining Crude Oil movements we estimated total Crude Oil inputs at refinery locations across the United States. We obtained data on refinery capacities and Crude Oil inputs from the EIA website. The refinery capacity data identifies refinery locations by state and city, while the inputs data only contain information at the PADD level. Thus, to calculate the state-level inputs data we constructed a PADD-county cross walk using the listing of PADD components from the EIA Petroleum Supply Monthly along with a GIS map of counties. This cross-walk was then used to allocate the PADD-level inputs data to states based on the refinery capacity shares.
We derive net movements of Crude Oil by pipeline from the above data by formulating an entropy maximization problem, with the entropy objective function based on the destination state shares of pipeline movements for each origin state. We used our estimates from the 2002 analysis to provide prior information in this objective function. This helps to incorporate information on actual pipeline infrastructure in our 1997 estimates. However, the entropy approach would reflect this information in the estimates only if it is consistent with available 1997 data and the following constraints:
- The matrix of state-to-state movements is set equal to the sum over all three modes i.e. rail, water, and pipeline, when the origin and destination states are not the same. For within state movements we allow the diagonal elements of matrix of state-to-state movements to be greater than the corresponding sum over the three modes. This helps to satisfy the other constraints in the programming problem, taking care of situations such as Alaska where its entire pipeline movement is within state. Any imbalance in the diagonal terms is added to the corresponding pipeline diagonal elements after the programming solution has been obtained.
- The sum of production and imports over destinations must equal the sum of the state-to-state matrix over destinations for each origin state.
- Refinery inputs in each destination state must be less than or equal to the sum of the state-to-state matrix over origins for that state. This constraint allows some states without refineries to receive Crude Oil.
This programming framework leads to a state-to-state pipeline matrix. However, when we examine the total pipeline numbers and compared to evidence from various sources (such as our estimates for 2002, consumption data for Crude Oil for 1997 and 2002, and other sources such as the AOPL) it became obvious that the pipeline number is underestimated by as much as 25 percent. This is apparently due to the fact that the balancing approach described above ignores other aspects of Crude Oil movement such as storage trips, multiple movements, and movements that take place near oil fields for which we have no data. Given this we escalated the state-to-state pipeline matrix derived from the calculation above by 25 percent.
4.2.4. Calculation of FAF-to-FAF Crude Oil Movements
The above calculation generates a complete set of state-to-state matrices for rail, water and pipeline. Disaggregating these data to FAF regions faces the same kind of challenges involved in the state level calculations above, and we adopted a similar approach by first disaggregating the water and rail data, and then deriving the net FAF-FAF movement for pipelines. Since the rail data is at the county level FAF-to-FAF rail movements were derived by simply aggregating the data to the FAF regions using a FAF-county crosswalk. For water, we made use of Manuscript Cargo data, also from the Corps of Engineers and the listing of major ports to derive an allocation mechanism. The Manifest Cargo data contains records of shipments and receipts by waterway codes (including port codes) for a number of commodities. Crude Oil is a separate commodity in this data. We extracted data for the 213 major ports whose locations were available for 1997 from this database, and associated county locations with each of these ports by checking each port name against place names from the Geographic Names Information System (GNIS). The county locations were summarized to FAF regions by state, and the receipts and shipments fields of the Manuscript Cargo data for Crude Oil were used to derive origin and destination vectors of water movements. FAF region shares in state movements were then calculated. Taking the product of the matrix of state-to-state water movements and the two vectors of shares, and summing over states for each FAF origin and destination combination produces a matrix of FAF-to-FAF water movements.
Since we do not have data on pipeline movements we derive its FAF-to-FAF matrix as the FAF-to-FAF matrix of all Crude Oil movements minus those for water and rail calculated above. In order to derive the FAF-to-FAF matrix of all movements we employ an approach similar to that used for water as described above. The origin and destination share vectors were derived by disaggregating the import and production data (for origin) and the refinery input data (for destination) to FAF regions for each state. For imports we used the port city to identify county locations, while we used the count of oil field codes from the EIA by county to allocate domestic production. For refinery inputs the site name (usually city name) was used to identify county of location. Once the counties are known we summarized each data source to the FAF level for each state and calculated the needed share vectors. The FAF-to-FAF matrix is then derived by taking the product of the sum of state-to-state movements over all three modes and the two share vectors, then summing over states for each FAF origin and destination combination. The FAF-to-FAF matrix of pipeline movements is then calculated by subtracting the water and rail matrices from this totals matrix. The net pipeline movements in a few cells were negative. These values were set to zero to derive our final pipeline movements matrix.
A comparison of the PADD level aggregation of the generated pipeline matrix (Table 4) to the EIA inter-PADD movements (Table 2) shows that the our estimates correctly identifies transfers from PADD 3 to PADD 2 as the main inter-PADD movement, but over predicts the other cells of the table.
| From | To |
|||||
|---|---|---|---|---|---|---|
|
PADD 1 | PADD 2 | PADD 3 | PADD 4 | PADD 5 | Total |
| PADD 1 | 356,815 | 1,832 | 7,976 | 0 | 0 | 366,624 |
| PADD 2 | 21,555 | 353,751 | 80,309 | 37,681 | 7,310 | 500,606 |
| PADD 3 | 480,248 | 938,452 | 2,241,918 | 56,583 | 17,694 | 3,734,895 |
| PADD 4 | 58,997 | 63,773 | 33,735 | 87,847 | 0 | 244,352 |
| PADD 5 | 13 | 22 | 9 | 46 | 584,232 | 584,323 |
| Total | 917,629 | 1,357,831 | 2,636,947 | 182,157 | 609,236 | 5,430,800 |
Note: This table also includes intra-PADD movements on the diagonal.
4.2.5. Truck
The only basis available for estimating Crude oil movement by truck for 1997 is Table 46 of the Petroleum Supply Annual. However, since this table was not available for 1997 we estimated our national truck movement number by comparing Table 46 for 1996 and 1998, which implied an estimate of about 65 million barrels. The AOPL also estimated total ton-miles by truck at 0.5 billion. Dividing the AOPL ton-mile number by the tonnage equivalent of the EIA number gives about 170 miles as the average distance of Crude Oil truck movement suggesting that these movements are short-hauls. We did not perform FAF level allocation for truck movements.
4.3. Expected Quality of the Estimates
The estimates are based on government data sources and, therefore, are expected to be reliable.
5. Implications for the Scope and Content of the 2007 CFS
There are multiple agencies that collect crude petroleum supply, disposition, and movement information. However, Crude Oil transportation statistics are not collected at a level of detail suitable for most transportation analysts. The proposed methodology can be used to generate reasonably reliable estimates of crude petroleum flow over different transportation networks. Therefore, it is recommended that the CFS 2007 should not change its sample frame to include crude petroleum movements. However, it is recommended that tables based on actual physical data on water, rail and pipeline infrastructure be developed for allocating state-level Petroleum Products data FAF regions.
6. References
Association of Oil Pipelines (AOPL), “Pipeline and Water Carriers Continue to Lead All Other Modes of Transport in Ton-Miles Movement of Oil in 2003,” Report, Washington D.C., May 16 2005.
Corps of Engineers (COE), 1997 State to State Commodity Movements from the Public Domain Database (Text format). Downloaded from the Web July 2005. http://www.iwr.usace.army.mil/ndc/db/wcsc/pdomain/data/.
FERC Annual Report (Form 6) of oil pipeline companies provided to the Federal Energy Regulatory Commission.
Corps of Engineers (COE), Waterborne Commerce of the United States, U.S. Army Corps of Engineers, Part 5, Table 2-2.
USDOT, Carload Waybill Statistics, Report TD-1, USDOT, Federal Railroad Administration and Freight Commodity Statistics, Association of American Railroads, Table A3.
