Annual Oil and Gas Extraction Survey (OGEX)
Detailed information for 2022
This annual survey collects information on Canadian companies involved in the oil and gas exploration, development and production industry. The survey collects financial, income and balance sheet information as well as operating statistics.
Data release - September 27, 2023
Data collected from businesses are aggregated with information from other sources to produce official estimates of national and provincial economic production for this industry.
Survey estimates are made available to businesses, governments, investors, associations and the public. The data are used to monitor industry growth, measure performance and make comparisons to other data sources to better understand this industry.
The survey is administered as part of the Integrated Business Statistics Program (IBSP). The IBSP has been designed to integrate approximately 200 separate business surveys into a single master survey program. The IBSP aims at collecting industry and product detail at the provincial level while minimizing overlap between different survey questionnaires. The redesigned business survey questionnaires have a consistent look, structure and content.
The integrated approach makes reporting easier for firms operating in different industries because they can provide similar information for each branch operation. This way they avoid having to respond to questionnaires that differ for each industry in terms of format, wording and even concepts. The combined results produce more coherent and accurate statistics on the economy.
Reference period: Calendar year
Collection period: April to August
- Business performance and ownership
- Crude oil and natural gas
- Financial statements and performance
Data sources and methodology
The target population is comprised of all establishments in Canada engaged in operating in exploration, development and production of oil and gas in Canada classified to the code 2111 according to the North American Industry Classification System.
The observed population is comprised of those establishments in the target population for which business information is available on Statistics Canada's Business Register and whose revenue exceeds a minimum threshold or cut-off. The cut-off excludes from the population all establishments that comprise the bottom 10% of an industry and/or geography grouping and is implemented to reduce response burden for small establishments.
The questionnaire was designed using Statistics Canada questionnaire design standards. The design was done in consultation with the surveys partners. The Annual Oil and Gas Extraction Survey questionnaire was redesigned for RY2018. The redesign consisted of the removal of non-relevant questions to reduce respondent burden. The use of provincial government data and data accrued from other surveys will act as supplements.
This survey is a census with a cross-sectional design.
The Business Register is a repository of information reflecting the Canadian business population and exists primarily for the purpose of supplying frames for all economic surveys in Statistics Canada. It is designed to provide a means of coordinating the coverage of business surveys and of achieving consistent classification of statistical reporting units. It also serves as a data source for the compilation of business demographic information.
The major sources of information for the Business Register are updates from the Statistics Canada survey program and from Canada Revenue Agency's (CRA) Business Number account files. This CRA administrative data source allows for the creation of a universe of all business entities.
The sampling unit is the enterprise, as defined on the Business Register.
Prior to the selection of a random sample, enterprises are classified into homogeneous groups (i.e., groups with the same industry and same geography) based on the characteristics of their establishments. Then, each group is divided into sub-groups (i.e. small, medium or large) called strata based on a size measure of the enterprise.
The sample size for reference year 2022 was 444 enterprises.
Data collection for this reference period: 2023-04-21 to 2023-08-25
Responding to this survey is mandatory.
Data are collected directly from survey respondents, extracted from administrative files and derived from other Statistics Canada surveys and/or other sources.
Collection method: Electronic questionnaire
Data capture method: The data from the questionnaire are processed directly into the Integrated Business Statistics Program (IBSP).
Follow-up method: Follow-up for non-response and for data validation is conducted by telephone or e-mail in order to reach the survey target response rate of 100%.
Languages offered to respondents: English and French
Time given to complete the questionnaire: 20 days
Average time required to complete questionnaire: 6 hours
1. Royalties and land lease sales information was requested from the provincial governments.
For the Province of Alberta, this information is retrieved from the Canadian Association of Petroleum Producers. Statistics Canada has a data-sharing agreement with them under the authority of the Statistics Act.
For Manitoba Department of Growth, Enterprise and Trade; Saskatchewan Ministry of the Economy, the British Columbia Ministry of Energy, Mines and Petroleum Resources and Indian Oil and Gas Canada, Statistics Canada has a data-sharing agreement with them under the authority of the Statistics Act.
2. Information for production volume is obtained from an internal program called Monthly Crude oil and Natural Gas (MCONG). Data for parts of this program are sourced from the Alberta Energy Regulator, via a data-sharing agreement under the authority of the Statistics Act.
Data integration combines data from multiple data sources including survey data collected from respondents, administrative data or other forms of auxiliary data when applicable. During the data integration process, data are imported, transformed, validated, aggregated and linked from the different data source providers into the formats, structures and levels required for IBSP processing. Administrative data are used in a data replacement strategy for a large number of financial variables for most small and medium enterprises and a select group of large enterprises to avoid collection of these variables. Administrative data are also used as an auxiliary source of data for editing and imputation when respondent data is not available.
Volume and value information is from the MCONG program.
View the Questionnaire(s) and reporting guide(s) .
Error detection is an integral part of both collection and data processing activities. Automated edits are applied to data records during collection to identify reporting and capture errors. These edits identify potential errors based on year-over-year changes in key variables, totals, and ratios that exceed tolerance thresholds, as well as identify problems in the consistency of collected data (e.g., a total variable does not equal the sum of its parts). During data processing, other edits are used to automatically detect errors or inconsistencies that remain in the data following collection. These edits include value edits (e.g., Value > 0, Value > -500, Value = 0), linear equality edits (e.g., Value1 + Value2 = Total Value), linear inequality edits (e.g., Value1 >= Value2), and equivalency edits (e.g., Value1 = Value2). When errors are found, they can be corrected using the failed edit follow up process during collection or via imputation. Extreme values are also flagged as outliers, using automated methods based on the distribution of the collected information. Following their detection, these values are reviewed in order to assess their reliability. Manual review of other units may lead to additional outliers identified. These outliers are excluded from use in the calculation of ratios and trends used for imputation, and during donor imputation. In general, every effort is made to minimize the non-sampling errors of omission, duplication, misclassification, reporting and processing.
When non-response occurs, when respondents do not completely answer the questionnaire, or when reported data are considered incorrect during the error detection steps, imputation is used to fill in the missing information and modify the incorrect information. Many methods of imputation may be used to complete a questionnaire, including manual changes made by an analyst. The automated statistical techniques used to impute the missing data include deterministic imputation, replacement using historical data (with a trend calculated, when appropriate), replacement using auxiliary information available from other sources, replacement based on known data relationships for the sample unit and replacement using data from a similar unit in the sample (known as donor imputation). Usually key variables are imputed first and are used as anchors in subsequent steps to impute other related variables.
Imputation generates a complete and coherent microdata file that covers all survey variables.
All units in the observed population whose revenue is above the minimum value (or "cutoff") for a particular industry and/or geographic grouping are surveyed. The cut-off or threshold for inclusion is selected to reduce response burden on those units in the population whose contribution to domain totals is deemed too small to be significant. Estimation of totals is done by simple aggregation of the values of all estimation units above the cut-off that are found in the domain of estimation. Estimates are computed for domains of interest such as industrial groups and provinces/territories, based on the most recent classification information available for the estimation unit and the survey reference period. It should be noted that this classification information may differ from the original sampling classification since records may have changed in size, industry or location. Changes in classification are reflected immediately in the estimates.
Prior to the data release, combined survey results are analyzed for comparability; in general, this includes a detailed review of individual responses (especially for the largest companies), general economic conditions, coherence with results from related economic indicators, historical trends, and information from other external sources (e.g. associations, trade publications or newspaper articles).
Data are collected under the authority of the Statistics Act, Revised Statutes of Canada, 1985, Chapter S-19.
By law, Statistics Canada is prohibited from releasing any information it collects that could identify any person, business, or organization, unless consent has been given by the respondent, or as permitted by the Statistics Act. Statistics Canada will use the information from this survey for statistical purposes only.
Revisions and seasonal adjustment
There is no seasonal adjustment. Data from previous years may be revised based on updated information.
All surveys are subject to sampling and non-sampling errors. Sampling error occurs because population estimates are derived from a sample of the population rather than the entire population. Non-sampling error is not related to sampling and may occur for various reasons during the collection and processing of data. For example, non-response is an important source of non-sampling error. Under coverage or over coverage of the population, differences in the interpretations of questions and mistakes in recording, coding and processing data are other examples of non-sampling errors. To the maximum extent possible, these errors are minimized through careful design of the survey questionnaire, verification of the survey data, and follow-up with respondents when needed to maximize response rates.
Measures of sampling error are calculated for each estimate. Also, when non-response occurs, it is taken into account and the quality is reduced based on its importance to the estimate. Other indicators of quality are also provided such as the response rate.
Both the sampling error and the non-response rate are combined into one quality rating code. This code uses letters that ranges from A to F, where A means the data is of excellent quality and F means it is unreliable. These quality rating codes can be requested and should always be taken into consideration.