Post-job analysis (PJA) has long been a cornerstone of operational learning in oil and gas — the process of consolidating lessons, verifying outcomes, and identifying improvements after a well intervention or plug-and-abandonment (P&A) job.
Traditionally, this meant lengthy meetings, manual reporting, and siloed data. Today, however, operators are taking a different approach. With real-time data streams, digital twins, and AI-driven analytics, PJA is becoming a dynamic, collaborative, and continuous process — one that directly supports safety, sustainability, and performance goals.
PJA is the structured review of how a job was executed and how well it met its objectives. It evaluates equipment, procedures, results, and key lessons learned.
Traditionally:
Service companies presented tools, procedures, and timelines of execution.
Operators reviewed performance against targets and outcomes.
Meetings were held long after completion, often relying on recollection rather than data.
This approach provided value but also introduced inefficiencies — slow feedback, inconsistent data, and limited collaboration. Modern PJA frameworks address these weaknesses through digital platforms that consolidate real-time operational information and make it accessible across all stakeholders.
The aim of PJA remains unchanged: to learn from each job, improve future performance, and reduce risk.
Historically, supervisors compiled post-job summaries weeks or months after completion. By then, key details could be forgotten, or data lost in disconnected systems. Digital collaboration platforms resolve this challenge by collecting sensor, tool, and performance data automatically and storing it in one shared environment.
The result is faster, richer insight. What once took weeks to prepare can now be analysed and reviewed within days, preserving operational knowledge and enabling proactive learning.
Read More: How to Automatically Track and Report all Activities and NPT Events for Wireline Jobs
A comprehensive PJA must answer two sets of questions:
Operational performance:
How did the procedure perform under actual conditions?
Were there equipment failures or anomalies?
Did execution follow the design and safety plan?
Results and objectives:
Were the forecasted outcomes achieved?
If not, what changed compared to previous campaigns?
What external factors, such as geology, weather, or crew composition, influenced performance?
Each job involves a wide range of variables, from tool configurations and subsurface conditions to vendor performance and environmental factors. In the past, consolidating these details required multiple workshops and manual spreadsheets. Today, digital systems automate this process and correlate information in real time, producing a complete picture of operational performance.
Traditionally, each stakeholder managed their own data: operators tracked production, service companies logged tool performance, and HSE reports lived elsewhere. The result was a fragmented view of the job.
Digital platforms now remove these barriers. By connecting everyone — operator, contractor, and service partner — in a shared, secure environment, PJA becomes a collective exercise. This mirrors the digital collaboration already commonplace in other industries, through tools like Microsoft 365 or Google Workspace.
Applied to well operations, this approach allows planning, execution, and review to take place in the same digital ecosystem, creating a single source of truth that supports efficiency, safety, and knowledge retention.
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The industry is rapidly transitioning from fragmented tools to integrated digital ecosystems. Real-time sensor data, IoT connectivity, and automated data capture are now standard in many drilling and intervention campaigns.
Key developments include:
IoT and sensor networks providing continuous operational data.
AI and machine learning predicting equipment failures and identifying anomalies.
Automated reporting that removes manual data entry.
Digital twins simulating operations before, during, and after execution.
These tools deliver real-time visibility and predictive insight, transforming PJA from a retrospective report into an active performance management process.
Modern PJA is no longer a one-off meeting. It’s a continuous improvement cycle:
Plan → Execute → Capture → Analyse → Learn → Improve
Digital collaboration platforms support this cycle by enabling:
Real-time data sharing among all stakeholders.
Standardised templates for structured PJA documentation.
Searchable “lessons learned” libraries.
Interactive dashboards for KPIs such as cost, NPT, HSE events, and production results.
These tools are reshaping upstream operations, turning data into actionable insight and reducing turnaround time between projects.
Post-job analysis is also becoming a key part of sustainability reporting. Digital platforms now include metrics for:
Carbon footprint tracking and emissions per operation.
Energy consumption monitoring during interventions.
Material use and waste reporting aligned with ESG frameworks.
By integrating sustainability into PJA, companies ensure that each lesson learned supports both performance and environmental goals.
To realise these benefits, organisations are aligning PJA processes with modern digital workflows. A consistent rhythm across the job lifecycle helps maintain structure and learning continuity:
Before the job:
Define KPIs such as time, cost, NPT, HSE, and production impact.
Standardise data collection formats and assign responsibilities.
Set up a shared workspace and reporting templates.
During the job:
Capture real-time data from sensors and service logs.
Record deviations and near misses directly in the digital platform.
Use AI-based monitoring to identify performance trends instantly.
After the job:
Conduct a “hot-wash” review within 48–72 hours.
Consolidate all information into a shared digital report.
Visualise performance against targets through dashboards.
Document “lessons learned” and assign actions for future improvement.
By embedding these steps into a collaborative workflow, companies build a digital knowledge base that enhances organisational memory, ensures accountability, and supports continuous improvement.
Additional reading: AI – How Can it Help Planning and Executing Well Intervention Jobs?
Doing more with less requires state-of-the-art solutions. Endless meetings and scrutinizing written reports is a thing of the past and we now look to the future of collaborative software solutions.
These solutions can increase turn-around on operations, while reducing risk and making accountability less ambiguous. However, we recognize that a super-tanker does not turn on a dime, which may be why we haven’t seen widespread adoption of such collaborative solutions in the industry. With the markets increasing demand for productivity and efficient, low emission solutions, solid analytics tools must be applied.
The goal is to take the whole supply and production chain of your wells into one digital platform. Knowledge, from multiple parties and subject matter experts, is always the foundation of such optimization. A collaborative software solution is how you extend the reach of that knowledge to make a meaningful difference.
Post-job analysis remains essential for operational excellence and continuous learning.
Digitalisation transforms PJA from a retrospective report into a real-time, data-driven process.
AI, IoT, and digital twins provide predictive insight and performance benchmarking.
Collaborative platforms enable shared access, accountability, and knowledge retention.
Integrating sustainability metrics ensures improvements support ESG goals.
The future of PJA is connected, intelligent, and continuous, helping every team learn faster, operate safer, and perform better.