Predictive Maintenance in Oil and Gas

Predictive Maintenance in Oil and Gas

Posted | Updated by Insights team:
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
  • Energy
  • Sustainable Growth and Tech Trends

Publication | Update: Sep 2020



The International Energy Agency’s gas market report, estimated that global gas demand could reach more than 4,100 billion cubic meters (bcm) in 2023.

According to Ayn de Jesus, AI Analyst at Emerj, AI could help oil and gas companies predict when their machines and equipment require maintenance, bearing in mind that long downtimes or employee injuries that could cost millions in legal fees and settlements.

  • Monitoring their machine assets
  • Predicting the probability of future machine failures
  • Making proactive maintenance decisions
  • And as a result, reducing operational costs arising from catastrophic machine failures

For instance, Uptake Technologies offers its Asset Performance Management (APM) application, which it claims can help oil and gas companies monitor their machine assets, predict future machine failures, and make proactive maintenance decisions using machine learning.

Uptake claims that the APM is driven by the Asset Strategy Library (ASL), a dataset containing data about machinery and equipment types, their failure mechanisms, as well as fluids and inspection data, fault codes, and operating thresholds.

The company states the machine learning model behind the software was trained on more than 800 asset types used in the energy, chemical, manufacturing, and mining industries, 10 million components, and the 58,000 ways they can fail.

The application can be applied on the edge and in the cloud. The company states that oil and gas experts at the client company would need to determine where to install sensors on the cylinder.

These sensors would then collect telemetric data from those parts of the cylinder, such as pressure. This data would then be used as a baseline for a properly functioning cylinder.

Industry giants Shell, ExxonMobil and BP are among those spearheading the use of artificial intelligence and internet of things (IoT) technologies to save costs and optimise machinery.

Data and analytics firm GlobalData says the companies are using the technology to evaluate the condition of their operational equipment and predict maintenance requirements, in order to reduce the likelihood of failures.

Its report, Predictive Maintenance in Oil & Gas, analyses how recent advancements in cloud-based data analytics and the rise of digital twins in oil and gas operations are extending the boundaries of predictive maintenance technologies.

Listed below are the key trends of predictive maintenance in the oil and gas industry, as identified by GlobalData.

Cost pressure

The oil and gas industry is just recovering from a major downturn that caused significant operating losses for the stakeholders. This lead to indefinite delays in planned projects bankruptcies and job losses. One of the important lessons derived from this downturn was that companies who excelled in managing operational expenditures had better chances of survival.

Traditionally, maintenance has been a recurring activity for the oil and gas industry aiding in asset productivity and profitability. However, periodic inspection and maintenance of assets are very costly. Additionally it still may not be fool-proof in avoiding unplanned equipment breakdowns. Adoption of predictive maintenance can help in early detection of faults in equipment, thus minimizing unplanned downtimes. It can also reduce the need to carry out scheduled maintenance activities periodically, thereby lowering the associated operational expenses.

Oil and gas companies were familiar with condition monitoring and predictive maintenance even before the slump, deploying them in many of their projects. However, the deployments were more in the case of new projects or as a part of asset modernization exercises. The oil price slump prompted companies to consider adopting predictive maintenance to improve operational efficiency and reduce equipment downtime.

Streamlining maintenance activities

Maintenance approaches in the oil and gas industry have transitioned from reactive to preventative and are now edging towards a proactive approach. The preventive maintenance approach that required inspection and maintenance at regular intervals was effective. However, it failed to completely eliminate machinery breakdown. Additionally, this approach required inspecting all equipment periodically, leading to redundancies in this approach. Also, it is very time consuming to determine the exact interval for conducting maintenance activities.

Predictive maintenance technologies help in adopting a proactive approach. They do this by identifying equipment that requires maintenance or servicing. It can also accurately diagnose the exact nature of a problem and suggest a possible solution to it. It thus enables maintenance personnel to selectively plan and undertake repairs. This reduces the overall effort required in conducting maintenance procedures. Predictive maintenance also contributes towards efficient management of machinery essentials, such as fluids, components, and other accessories.

Ageing infrastructure

Equipment and infrastructure presently in use for oil and gas operations was constructed several decades ago. It was based on designs, materials, and technologies available at that particular time. In some instances, these ageing assets have surpassed their predetermined life expectancy envisioned during the design phase. Such ageing equipment is bound to fail and therefore requires constant inspection and monitoring. Installing sensors on ageing equipment can also avert potential accidents and minimize risks to the workforce and the environment.

However, implementing predictive maintenance in older equipment requires incorporating sensors and cabling to capture data. In contrast, most new equipment come pre-equipped with sensors. These monitor telemetry and other vital statistics that can be directly fed to a predictive analytics system.

Shortage of skilled workforce

Some predictive maintenance technologies have been in existence for a while, and oil and gas companies have implemented them in a variety of environments. However, in the last few years, there has been a rise in adoption of predictive maintenance technologies within the industry to ensure all the field equipment are functioning at their optimum levels.

However, the availability of a trained workforce necessary for analyzing sensor data may prove to be inadequate, considering the growing demand for predictive maintenance services worldwide. Evaluating data generated from vibration sensors, thermographic equipment, and other devices requires in-depth domain knowledge and expertise, which requires years of training and field experience.

This is an edited extract from the Predictive Maintenance in Oil & Gas report produced by GlobalData Thematic Research.

Read More on Vendors and Use-Cases: https://emerj.com/ai-sector-overviews/predictive-maintenance-oil-and-gas/

Nine upstream companies using predictive maintenance in the oil and gas industry: https://www.nsenergybusiness.com/features/predictive-maintenance-oil-and-gas/

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Objectives and Study Scope

This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.

The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges. The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period. The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the  market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players.  This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.

Research Process & Methodology


We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.

We draw on available data sources and methods to profile developments. We use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites.
Historical, qualitative and quantitative information is obtained principally from confidential and proprietary sources, professional network, annual reports, investor relationship presentations, and expert interviews, about key factors, such as recent trends in industry performance and identify factors underlying those trends - drivers, restraints, opportunities, and challenges influencing the growth of the market, for both, the supply and demand sides.
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Research Portfolio Sources:

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  • Oxford Economics: Global Industry Forecasts, Country Economic Forecasts, Industry Forecast Data, and Monthly Industry Briefings

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M&A and Risk Management | Regulation

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Forecast methodology

The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.

Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

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As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.

Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.

Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pre­tax revenue and its total bought­in costs (costs excluding wages and salaries).

Forecasts of GDP growth: GDP = CN+IN+GS+NEX

GDP growth estimates take into account:

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  • Investment as a function of the return on capital and changes in capacity utilization; Government spending as a function of intervention initiatives and state of the economy;

  • Net exports as a function of global economic conditions.


Market Quantification
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.


Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.

Industry Life Cycle Market Phase

Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:

  • Nascent: New market need not yet determined; growth begins increasing toward end of cycle

  • Growth: Growth trajectory picks up; high growth rates

  • Mature: Typically fewer firms than growth phase, as dominant solutions continue to capture the majority of market share and market consolidation occurs, displaying lower growth rates that are typically on par with the general economy

  • Decline: Further market consolidation, rapidly declining growth rates


The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.

The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.

  • Vector Auto Regression (VAR) statistical models capturing the linear interdependencies among multiple time series, are best used for short-term forecasting, whereby shocks to demand will generate economic cycles that can be influenced by fiscal and monetary policy.

  • Dynamic-Stochastic Equilibrium (DSE) models replicate the behaviour of the economy by analyzing the interaction of economic variables, whereby output is determined by supply side factors, such as investment, demographics, labour participation and productivity.

  • Dynamic Econometric Error Correction (DEEC) modelling combines VAR and DSE models by estimating the speed at which a dependent variable returns to its equilibrium after a shock, as well as assessing the impact of a company, industry, new technology, regulation, or market change. DEEC modelling is best suited for forecasting.

Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from official sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reflect different assumptions about their relative importance.


The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.

Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.

Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.