Annual Oil and Gas Extraction Survey (OGEX)

Detailed information for 2016





Record number:


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 - October 6, 2017


These data are required for integration into the input-output sector of the Canadian System of National Accounts. Data are intended for use by survey respondents, federal departments and agencies, provincial ministries and authorities, industry associations, industry analysts, the press and the general public to assess trends in the oil and gas extraction sector of the Canadian economy.

Statistical activity

The survey is administered as part of the Integrated Business Statistics Program (IBSP). The IBSP program 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: Late March to late July


  • Business performance and ownership
  • Crude oil and natural gas
  • Energy
  • Financial statements and performance

Data sources and methodology

Target population

The target population is comprised of all establishments in Canada engaged in operating in exploration, development and production of oil and gas in Canada (NAICS 21111) 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.

Instrument design

The questionnaire was designed using Statistics Canada questionnaire design standards. The design was done in consultation with the survey's partners.

The questionnaire is respondent completed electronically. The questionnaire is also available in paper format delivered by mail or fax, and respondents can complete it over the phone with interviewers.

The questionnaires are subject to regular revision to reflect changes in information aspect requirements.


This survey is a census with a cross-sectional design.

Data sources

Data collection for this reference period: 2017-03-27 to 2017-07-31

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 (& Method of initial contact): electronic questionnaire
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 potential respondents: English and French
Time given to complete the questionnaire: 20 days
Average time required to complete questionnaire: 10 hours

1. Royalties and land lease sales information are from the specified province's budget for the current reference year.

For Province of Alberta, this information is retrieved via the client, the Canadian Association of Petroleum Producer. StatCan has a data-sharing agreement with them under section 12.

For Province of Manitoba (Department of Mineral Resources); Saskatchewan Ministry of the Economy; the British Columbia Ministry of Natural Gas Development; and Indian Oil and Gas Canada; StatCan has a data-sharing agreement with them under section 12.

2. Information for production volume is obtained from an internal program, Monthly Crude oil and Natural Gas (MCONG). Data for parts of this program is sourced from the Alberta Energy Regulator (AER), via data-sharing agreement (section 13).

Based on volume information based on MCONG program, the values of oil and gas products were derived are computed by applying a given price for each product for four provinces (Newfoundland, Saskatchewan, Manitoba and Alberta). For the remaining provinces (Nova Scotia, British Columbia), calculations were done using a given ratio relative to Alberta.

View the Questionnaire(s) and reporting guide(s) .

Error detection

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.


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.

Quality evaluation

In order to ensure the accuracy and consistency of the data, the results of the survey are reconciled with other information provided by the Canadian Association of Petroleum Producers (CAPP). Other federal departments, provincial and territorial authorities routinely monitor the data.

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects which could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act. Various confidentiality rules are applied to all data that are released or published to prevent the publication or disclosure of any information deemed confidential. If necessary, data are suppressed to prevent direct or residual disclosure of identifiable data.

Revisions and seasonal adjustment

Revisions may be performed for the previous year.

Data accuracy

For a cut-off census, the main source of error in statistical estimates is due to non-response. Non-response bias is minimized by making special effort during data collection to encourage non-respondents to reply to the questionnaire. In cases where imputation is required, imputed data is carefully reviewed to ensure validity and consistency with current and any previously reported data that is available.

If changes are received from respondents, the data are incorporated and the disseminated data are revised.

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