Re-printed from OILFIELD TECHNOLOGYAugust 2011

Sebastiano Barbarino, OVS Group, USA, explains how

asset performance can be increased through workflow technology utilising existing information and applications.

Delivering on

Pemex’s Samaria-Luna asset, located in the southern state of Tabasco in Mexico, produces

> 200 000 bpd, with over 200 wells on both natural flow and gas lift. Gas for lift operations is supplied by skid-mounted booster compressors at the wellhead.

The challenges x Monitoring useable data from

multiple and often conflicting sources of data.

x Integrating results with operational data from modelling applications provided by multiple vendors.

The solution One Virtual Source (OVS) technology combined automation of modelling workflows, real time monitoring, surveillance-by-exception (SBE), and virtual integration of eight data sources, including real time, operational, well test, field data, and others, into a single and user-friendly environment.

data sources and applications

Re-printed from OILFIELD TECHNOLOGYAugust 2011

Re-printed from OILFIELD TECHNOLOGYAugust 2011

The results x Standardised engineering processes across the asset, with

a heavy emphasis on automation and analytics.

x Integrated reporting and SBE methods ensure engineers’ time is now focused on engineering issues, not data gathering.

x ‘Live links’ to all sources ensure that data duplication is no longer an issue.

x Workflow-based automation has brought the various preexisting well, production network, and reservoir modelling tools online, so they can be incorporated into daily operational activities and decision making.

The ‘intangible’ benefits of standardising processes and providing readily available access to information are seen as key enablers of the more directly measurable economic benefits.

BackgroundThe Samaria-Luna asset has multiple data sources – eight primary data sources that collect information including operational data, official data, real time data, laboratory fluid samples, corporate data and various data files in Excel for nitrogen, well test, reservoir pressures, among others.

Well models are maintained to support decisions regarding production schedule changes, well performance analysis and optimisation efforts. This necessitates that the primary well models are maintained by a single owner to avoid inconsistency in updates and interpretations.

A large number of man hours were required to assemble the necessary information before effective analyses were made of changing well performance trends and problem predications. This also applied to the effort required to update and maintain the well models in an ‘evergreen’ state.

The primary requirements of this project were to deliver a software platform for engineering workflows and analytics that would:

x Virtually integrate all information from the Samaria-Luna asset. A virtual integration was preferred in order to avoid the high costs and disruption of migrating to a data warehouse.

x Perform unattended SBE of critical reservoir and operational variables using both user and model-defined operating envelopes.

Figure 1. The key building blocks to improving asset performance.

x Deliver engineering workflows to automate and standardise key engineering processes, including nodal analysis, gas lift optimisation, and virtual well metering.

x Assure rapid uptake of the new software platform by providing an innovative and user-friendly interface capable of providing a complete management solution from acquisition through analysis to decision.

Workflow automationThe asset required three types of workflow automation:

Surveillance-by-exception automation. The SBE requirement is to provide a system for unattended monitoring of critical operational data. Exceptions to operating envelopes will result in alarms and email-based notifications to ‘asset owners’ when critical situations are identified. Critical to the success of such a solution, the operating envelopes would be defined based on both user-adjustable and model-based criteria.

Prior to implementation, it was noted that the varied sources of data could present a potential challenge to successfully delivering this requirement. As not all wells were equipped with real time sensors, the SBE system had to be intelligent enough to recognise the correct data source on a well-by-well basis (e.g. real time sensors vs. field data capture). Pemex’s technical team also desired that the same workflow methodology be employed across the entire asset; all evaluation and reporting had to be standardised, irrespective of the actual data source in use. Once identified, this challenge could be designed for and easily addressed during implementation.

Well modelling automation. The primary requirement for well modelling automation was to provide the capability to automatically run well-by-well optimisation using existing modelling tools. In order to achieve this, a number of individual workflows were required:

x Well test validation.

x Sensitivity analysis for model fine-tuning.

x Model adjustment.

x Optimisation by well.

x Tubing head pressure validation.

It was required that all of these workflows be sufficiently autonomous to automatically run when a new well test is acquired. The workflows must keep a log of the results and store the historical evaluations so the engineers could later analyse them in conjunction with alarms and other available operational data.

Virtual well metering. Virtual well metering was a further requirement for the system. Using the ‘evergreen’ models, the system had to generate estimates of individual three phase well flow rates based on the current operation. The desired frequency of the virtual flow rate estimates was daily.

The technologyThe overarching principle of this project was to rapidly deliver a user-friendly solution for accessing and analysing data, and automating standard engineering workflows. Pemex desired to preserve and utilise all existing data and application infrastructure to minimise the technical and economic costs of the project. This approach would provide the immediate benefits of a fully integrated solution with a minimum of time and effort.

Re-printed from OILFIELD TECHNOLOGYAugust 2011

The solution implemented was based on the use of the commercial platform OVS that had been successfully implemented in a number of other assets in Pemex. The platform addressed the requirement to integrate all existing data sources and applications into a single working environment, without changing the existing infrastructure. Within weeks, the virtual data integration effort was complete; the final project, including workflow delivery, was successfully concluded within four months – an unprecedented achievement.

Implementation

Data integration phaseOnce the requirements for the project had been defined, implementation began by identifying and mapping all of the available data sources into the software platform. The data sources integrated were: SISRED, SICAVHI, SNIP, FINDER, OSIsoft PI, well test, reservoir pressure and nitrogen injection.

In this early stage, it was noted that the critical well test data was dispersed in a variety of Excel spreadsheets maintained by different areas, all stored in different formats. Effort was undertaken within the asset organisation to standardise the process for well test data capture using the OVS platform, and storing the data to a common database. This critical data was made available at any time, and served as the starting point for many of the new workflows that were later developed. Migration of historical data to this common data source also facilitated standardised reporting and visibility of well test data across the asset for all areas.

Access to real time data stored within the OSIsoft PI historian provided much needed visibility into live data feeds for the wells and booster compressors, which could be viewed in real time within OVS, alongside other key data from different sources and at different frequencies.

The implementation was developed in such a way as to comply with a logic that represented the standard engineering workflow adopted by this organisation for production optimisation and surveillance (Figure 2). Integrated displays and reports provide significant support to daily operational and strategic decision making.

Surveillance phaseUsing the integrated data access layer configured in the previous phase, SBE criteria were configured to monitor data from

any available source in an unattended fashion. These criteria consisted of complex, compound conditions that could be tuned by end users, as well as model-based predictions.

Pemex’s technical team specified a standardised set of criteria for evaluating the critical operational variables on a well-by-well basis. These same standards were applied in a transparent fashion to end users, regardless of whether data was captured manually or automatically by sensors.

The OVS platform’s support for dynamic data sources proved critical to meeting the requirements for standardised SBE criteria and displays. With no additional input required by the end user, the data integration layer transparently determines the availability of data and the appropriate source from which to read. All calculations and reporting have been standardised across the asset, despite the underlying complexity.

Asset wide visualisationWith surveillance in place, standard reports and dashboards were then delivered to increase visibility of performance metrics across the asset. These reports combined roll ups of data and alarms from all available sources for an integrated view of asset performance – a view previously unavailable due to the disparate systems.

Engineering workflow automationWith the foundational data access capabilities in place, configuration of the required engineering workflows could proceed. The intent of the workflows was to not only reduce the time spent manually gathering data, but to implement standardised analysis procedures supported by automation. For this project, the workflows addressed nodal analysis for both gas lifted and naturally flowing oil wells.

Using a commercial nodal analysis application, the following workflows were addressed:

x Well test validation.

x Sensitivity analysis for model fine tuning.

x Model adjustment.

x Optimisation by well.

x Tubing head pressure validation.

x Virtual measurement.

While some of these processes had been previously performed manually, the time required for data collection, model updates, and aggregating the results in Excel was prohibitive for frequent analysis. Supported by the OVS workflow automation, these processes are now performed automatically so engineers can focus on value-added engineering work.

Well test validationAfter every well test was taken and updated to the database, the results were used to automatically validate whether the accuracy of the model was within a user-defined tolerance. This process was critical to establishing the validity of the model to be used in further processes, such as optimisation and virtual metering.

The workflow gathers the operating variables at the time of the well test, including THP, Qgi, GOR and water cut and calculates the model predicted oil volume. If the model predicted result was within a certain tolerance of the measured volume, then the model was compliant and could be relied upon for further analysis. The workflow also extracted the model

Figure 2. Example of engineering workflow for production optimisation and surveillance.

calculated performance curves so they were available for comparisons and display.

The workflow was scheduled to run automatically as new well test data became available. No human action was required for this process to be performed. The calculations were still performed using the existing nodal analysis software and models; OVS introduced the automation necessary to effectively run this analysis on a wide scale.

Sensitivity analysis for model fine tuning To better support the process of fine tuning the well models, a workflow was developed to standardise the model parameters which may require updates. The workflow iteratively sought the proper adjustment to independent parameters necessary to reproduce the test condition (e.g. 2% decrease in reservoir pressure). Further reports were also available to assist the engineer in validating this result using available data (e.g. historical reservoir pressure measurements and forecasts).

For Samaria-Luna, the process was standardised to consider six key model parameters, including:

x Gas/oil ratio.

x Water cut.

x Gas injection rate.

x Tubing head pressure.

x Choke.

x Reservoir pressure.

These model parameters were all selected based on level of importance and measurement uncertainty.

Once the engineer had decided which parameter to adjust, the adjustment was applied through the OVS Model Manager. The OVS Model Manager recorded all changes and adjustments made to the model so the audit trail could be consulted at any time. Prior versions of the models were also archived for use in future analysis and could be rolled back to undo a change if necessary.

Optimisation by wellUsing the validated well model, an optimisation process could then be run to identify the optimal operating condition for each well and quantify the resulting gain in oil production.

This standard process was automatically run every day for every active well, using the well’s current operating parameters

(WHP and Qinj Rate). The process is identical for wells with real time data and those without.

To determine the optimal operating conditions, an iterative process is run to calculate the well’s maximum effective production rate. The algorithm employed searched for the asymptotic maximum, as opposed to the technical maximum, in order to make the most efficient use of gas. The constraints were configurable by the end users, and could be changed at any time.

Tubing head pressure validation As a further check on the validity of the model, the model-predicted tubing head pressure was calculated using the production line pressure measurements. If the model-predicted THP is within a user-defined tolerance of the measured THP (e.g. 10%), the results of the optimisation were considered valid. If that validation failed, an alarm was generated and displayed as an indication that the engineer could review the data and/or model prior to applying any operational changes.

Virtual well metering Using the ‘evergreen’ well models, a virtual well metering workflow was run to predict the daily three phase flow rate for each well. The flow rates were calculated based on the daily operating conditions of each well.

ConclusionThe new production surveillance and optimisation tool described has seen rapid uptake within the Samaria-Luna asset. The tool is in daily use by engineers, and provides much needed visibility into daily operations, enabling early response to problems and proactive well management.

Integrated displays, unattended surveillance, and automated engineering workflows have all contributed to more effective decision making. Processes have been standardised, data is readily available, and uncertainty has been reduced. Productivity of the engineers has greatly improved, and quantifiable benefits are being realised.

The implementation has actively supported the continuous growth and maintenance of production for this asset. Further work is planned to employ this same technology to support both reservoir engineering and well planning activities, with similar anticipated benefits. O T

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