Data Analysis and Neuro-Fuzzy Technique for EOR Screening : Application in Angolan Oilfields

This study is sponsored by the Angolan National Oil Company (Sonangol EP) and the authors are grateful for their support and the permission to use the data and publish this manuscriptPeer reviewedPublisher PD

Intelligent grain size profiling using neural network and application to sanding potential prediction in real time.

Production of hydrocarbon from both consolidated and unconsolidated clastic reservoir rocks poses a risk of sand production especially if a well articulated programme of sand management strategy is not put in place to deal with the problem at the onset of field development. A well articulated programme of sand management would include sand production potential prediction in real time if it is going to be effective at all in achieving the goal of dealing with likely sand problem. Sanding potential prediction in real time is considered an element of sand management strategy that involves the evaluation of risk of sand failure/production and the prediction of the likely sand rate and volume to facilitate optimum design of both downhole and surface equipment especially as related to sand control. Sanding potential prediction is therefore very crucial to reducing costs of field developments to make hitherto unattractive development environments profitable. This undoubtedly will impact positively the present drive to increase worldwide production of hydrocarbon . Specifically, real time sanding potential prediction enables timely reservoir management decisions relating to the choice, design and installation of sand control methods. It is also an important input to sand monitoring and topside management. The current sanding potential prediction models in the industry are found to lack the robustness to predict sanding potential in real time. They also are unable to provide the functionality to track the grain size distributions of the sand producing formation and that of the produced sand. This functionality can be useful in the application of grain size distribution to sanding potential prediction. The scope of this work therefore covers the development of coupled models for grain size distribution and sanding potential predictions in real time. A previous work has introduced the use of a commercial neural network technique for grain size distribution prediction. This work has built upon this by using a purposefully coded neural network in conjunction with statistical techniques to develop a model for grain size distribution prediction in both horizontal and vertical directions and extending the application to failure analysis and prediction of strength and sanding potential in formation rocks. The theoretical basis for this work consists in the cross relationships between formation petrophysical properties and grain size distribution parameters on one hand and between grain size distribution parameters and formation strength parameters on the other hand. Hoek and Brown failure criterion, through an analytical treatment, serves as the platform for the development of the failure model, which is coupled to the grain size distribution and Unconfined Compressive Strength (UCS) models. The results obtained in this work have further demonstrated the application of neural network to grain size distribution prediction. They also demonstrate that grain size distribution information can be used in monitoring changes in formation strength and by extension, the formation movement within the failure envelope space especially during production from a reservoir formation

Improving the simulation of a waterflooding recovery process using artificial neural networks

The waterflood performance of the dual five-spot pilot project in the Stringtown oil field, situated in West Virginia, has been studied. A numerical simulator, called BOAST98, was used for the simulation purposes, after developing a reservoir description.;The producing horizon in the field is the Upper Devonian Gordon sandstone, which is characterized by severe heterogeneity due to the depositional environment. Using available core and log data and geological analysis, a reservoir characterization study was done. A preliminary reservoir description based on log porosity-core permeability correlation was improved by developing Artificial Neural Networks (A.N.N.), which incorporates geophysical well log information. These A.N.N.\u27s were utilized to predict porosity and permeability for five wells in the pilot area.;A reservoir model for simulation purposes was constructed after identifying the principal flow units within the formation. Results from the simulation were compared with five years of actual field data. A close history matching for the cumulative oil and water production in the pilot project was achieved after scheduling 10--15% of the total water injection volume into the pilot area