AbstractWell work-over activity to optimize the oil well production is a very cost intensive activity. Before performing any well work over activity such as perforation, stimulation, cementing, snubbing & swabbing, it is essential to evaluate the same in terms of return on investment (ROI). Selection of best suited well work over activity depends upon various oil well parameters such as rate gain estimates, reserve estimates and cost estimates. This white paper talks about the solution which will address the missing functionalities in the prevailing well work-over systems used for production optimization.

Work Overs for Production Optimization

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Purpose of the documentWell work-over activity to optimize the oil well production is a very cost intensive activity. Before performing any well work over activity such as perforation, stimulation, cementing, snubbing & swabbing, it is essential to evaluate the same in terms of return on investment (ROI). Returns can be in form of rate gain estimates and reserve estimates and the investment may be the actual cost involved in performing well work over activity on oil well.

Well services engineer faces great difficulty while selecting the best suited well work over activity. Selection of best suited well work over activity depends upon various oil well parameters such as rate gain estimates, reserve estimates and cost estimates.

Currently most of the oil & gas clients make use of the human reasoning & past experience to formulate the work-over strategy to optimize the well productivity. Some of the clients make use of the data mining techniques to estimate the possibility of success of their work-over strategy, but these kinds of techniques only perform the qualitative analysis based on the similarity factors for the different well parameters.

Therefore data mining techniques are not considered to be very good analysis techniques as they often lead to under-estimation or over-estimation of possibility of success due to absence of the quantitative element. Adding a quantitative element would mean configuring some business rules to define the criticality & importance of well parameters from the work-over perspective.

It is seen that most prevalent systems for such kind of analysis are standalone systems, which is only used by the primary users having a direct access to that process. These systems restrict the free flow of information to other stakeholders, who are more experienced & should be a part of this process to make business critical decisions. Absence of the collaborative platform for information sharing leads to rework on a particular well & decreased cash flows. These systems also fail to provide the integration points for 3rd party applications and thus restrict data flow from/to these applications.

Current systems used in the industry are also missing out on a very important functionality which is building of knowledge repository as they fail to capture the knowledge gained out of the historical real live experience. This knowledge base is very important from the

point of view of resolving future problems as it acts as a major guiding factor for the process stakeholders while making business critical decisions.

This white paper talks about the solution which will address the missing functionalities in the prevailing well work-over systems used for production optimization.

Business NeedThere are various challenges customers face when the reservoir production starts declining with time. Some of them are listed below.

1. Identification of appropriate well work-over technique to optimize well productivity.

2. Achieving economies of scale while performing work-overs.

3. Formulating strategy for the selection of best suited work practices to have the best possibility of success (POS).

4. Estimation of possibility of success (POS) and other parameters like Pre-job rate gain estimates, reserve estimates, cost estimates, which are essential to evaluate the request before commencing the work-over.

5. Availability of knowledge base to identify & analyze potential opportunities to improve the performance of the well.

6. Collaborative platform for on-line sharing of industry best practices, guidelines, check sheets and lesson learned.

7. Automated Post work Success analysis and report generation.

8. Chances of underestimation or overestimation of POS for a particular work type.

9. In oilfield, field personnel normally solve the problem by human reasoning & miss out on the important value, which data analytics rules can add to it.

10. Managing & tracking all the phases of well work request right from its initiation till the execution to avoid the rework that are done in wells at different locations.

11. Quality of well work requests submitted to well services department.

DescriptionWork-overs are activities aimed at improving Oil and gas extraction by leveraging the capabilities of equipments employed. A typical well facility involves enormous investments in the assets and well work-overs involve huge capital expenses which have the potential of reducing operational cost significantly. Right decision making regarding work overs can result in considerable production gain thus improving profitability.

The stakeholders need to

Identify the potential opportunities.

Analyze the work-over request

Calculate the Success rate for a work-over request.

Refer to case histories for finding solutions or evaluation of similar work-overs done in past.

Store all the solutions in a database for future reference

The proposed well work-over solution is a decision support system enabled with CBR (Case Based Reasoning) engine. The work-over solution can be used by the Petroleum Engineers to identify and implement appropriate work-over on an asset, whenever a possible potential to enhance the performance of the asset is identified by PE. This solution can be used to create work-over request and to get the post work-over reports for the existing requests. Post work success analysis helps the petroleum engineer to evaluate the success or failure of the work-over done on any asset by comparing the well parameters before and after the work-over.

Some of the main functionalities exhibited by this solution are:

Online Well work-over request process with all pre-job estimates for Reserves, Flow rates and Cost

Case-based Reasoning approach to estimate success rate for the proposed work

Work-over request ranking system to identify potential and critical works

Strict monitoring on time, efforts and resources

On-line sharing of industry best practices, guidelines, check sheets and lesson learned

Automated PWSA (Post work success analysis) and report generation

This work-over solution can be an accurate decision tool for the stakeholders such as petroleum engineers to evaluate the success rate for a decision regarding well work-over. The system will timely request processing and implementation by setting time limit for various phases of well work-over request. Post work success analysis is incorporated for evaluation of success rate of the completed work-over and is stored as a case for future reference.

Petroleum Engineer (PE) can analyze the current well performance and determine well work-over opportunities. For issuing a work-over request, PE can evaluate and generate feasibility of various well work-over requests and review best practices guidelines & checklist. Petroleum Engineer (PE) can generate a WWSR (Well work-over service requests), authorized by peer reviewer after relevant reviews. Well Service Engineer (WSE) can take approved WWSR for execution. Post Work Success Analysis (PWSA) can be performed by the Petroleum Engineers to identify if the work that they executed on the wells were a success or not.

The solution can help petroleum engineer to identify potential opportunities to improve the performance of the well by estimating key success parameters such as success rate (SR) and pre-job estimates, which are essential to evaluate the request before commencing the work-overs.

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Results/ConclusionThis solution will perform certain calculations based on the configurable business rules defined in the business logic layer of the solution and these calculated well performance parameters will help process stakeholders make business critical decisions to optimize asset performance. Some of the critical well performance KPIs generated by the solution can typically be

Barrels oil equivalent (BOE) rate gain due to intervention before actual work-over

Incremental estimated ultimate recovery (EUR)- Gas/Oil before actual work-over

Incremental estimated ultimate recovery (EUR)- Barrels oil equivalent (BOE) before actual work-over

Cash flows due to incremental reserves

Ranking before actual work-over

Barrels oil equivalent (BOE) rate

Possibility of success (POS)- Recommended by solution

REFERENCES

www.stottlerhenke.com/ai_general/glossary.htmCached - Similar- Stottler Henke

Bogaerts, S., and Leake, D. 2005. A Framework for Rapid and Modular Case-Based Reasoning System Development. Technical Report TR 617, Computer Science Department, Indiana University, Bloomington, IN.

Bogaerts, S., and Leake, D. 2006. What Evaluation Criteria Are Right for CCBR? Considering Rank Quality. In T. R. Roth-Berghofer et al., eds., LNAI 4106, Proceedings of the Eighth European Conference On Case-Based Reasoning, pp 385-389, Berlin Heidelberg. Springer-Verlag.

Stakeholders Petroleum Engineer (PE)- Responsible for

creating the WWR and monitoring the progress and status of the outstanding WWRs

Best Practice Lead (BP Lead)- Responsible for assigning the Peer Reviewer for the WWR

Peer Reviewer (PR)- Responsible for reviewing and signing-off WWRs submitted by the Petroleum Engineers

Well Service Engineer (WSE)- Responsible to carry out a work on assigned WWR

Value Proposition for E&P Companies1. E&P companies are facing the

challenge of losing the knowledge and skills when a key employee leaves By having CBR based model and by storing the valuable experiences in structured way, E&P companies can overcome the barriers.

2. Through this solution customers will come to know about the incremental cash flows i.e. actual return on investment generated out of incremental oil/gas reserves in a simulated environment without investing anything before actual work-over implementation, which will be a recurring saving for customer for each work-over against one time total investment.

3. This solution will help customers save millions of dollars by suggesting the appropriate work-overs & avoiding the rework on a particular well.

4. This solution will allow business users involved in this process to spend more time in productive jobs by saving lot of their time spent earlier in routine activities due to complete work-over process automation.

5. This solution makes use of the case based reasoning (CBR) modeling technique in addition to the human reasoning & past experience & thus reduces the chances of deviation of

success (POS) for a particular work-over on a particular well.

6. Through its collaborative platform this solution will enable free flow of information to all the business users to help them make business critical decisions.

7. This will be a highly customizable solution which will also provide easy integration with other 3rd party applications to enable information flow from/to applications.

8. This solution would also help in building knowledge base by storing solved problem cases back into the case historian, which can be used as a reference while solving the future problems.

9. Selection of optimum solution through comparative analysis with similar problem cases in the past and considering relevant parameters like past success rate.

10. Online reporting & knowledge sharing.

11. Eliminates redundant data sets.