Core Data Preprocessing To Improve Permeability Log Estimation
- Mauro Cozzi | Livio Ruvo (ENI Agip SpA) | Paolo Scaglioni (ENI E&P) | Anna Maria Lyne (ENI E&P)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 24-27 September, San Antonio, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2006. Society of Petroleum Engineers
- 1.6.9 Coring, Fishing, 5.1 Reservoir Characterisation, 5.1.3 Sedimentology, 5.6.1 Open hole/cased hole log analysis, 5.8.7 Carbonate Reservoir, 4.1.2 Separation and Treating, 5.5.3 Scaling Methods, 6.1.5 Human Resources, Competence and Training, 4.1.5 Processing Equipment, 5.6.4 Drillstem/Well Testing, 1.2.3 Rock properties, 4.3.4 Scale
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Two techniques of preprocessing data from core plugs have been investigated to enhance the quality of synthetic permeability estimation from conventional logs using Artificial Neural Networks (ANNs). A first technique consists of 'cleaning' the core plug data set by removing the measurements deemed as log-incompatible, i.e. those from plugs corresponding to log measurements affected by shoulder-bed effect, and those from layers with thickness below the vertical log resolution. The second technique relies on building high-resolution digital models of cored intervals using a Process-Oriented Modeling (POM) approach: the core model is populated with permeability values from core plugs and then upscaled to a log-equivalent support volume.
Synthetic permeability curves estimated with the above techniques have been compared to synthetic permeability curves estimated without core-data preprocessing and to permeability estimated directly from core plugs and properly calibrated permeability curves from a Nuclear Magnetic Resonance log tool in a turbidite reservoir, the ground truth value being represented by actual dynamic data. Results highlight that core-to-log scale-effects play a major role in the permeability estimation from conventional logs and show that the proposed preprocessing techniques can be very effective in improving permeability prediction, as they significantly reduce cross-scaling problems related to the differences in support volumes.
Strengths and weaknesses of the two preprocessing approaches have also been compared. The first technique is faster, but its application can be strongly constrained by statistical and geological representativeness of the selected data set, in the sense that some lithologies could go under-represented so as to question the use of estimation tools like ANNs. Conversely, the POM preprocessing technique is more time-consuming and needs detailed core descriptions, but has the great advantage to supply - starting from core data only - a reliable permeability curve that can be retained valid at the log scale.
Permeability prediction in hydrocarbon reservoirs is probably the most challenging issue geologists, petrophysicists and reservoir engineers have to deal with. In particular, the availability of permeability curves in a large number of wells is one of the most desired targets in a reservoir characterization study. In the last years, logging techniques such as Nuclear Magnetic Resonance (NMR) have been developed that allow the generation of permeability curves along reservoir intervals. Nevertheless, the availability of NMR logs is not the rule: in the majority of the wells, especially those from older fields, the only permeability measurement available comes from plugs sparsely sampled from bottom-hole cores. On the other hand, bottom-hole cores are usually available only in a few reservoir intervals and/or wells, whereas conventional log recordings (natural gamma-ray, density, neutron, etc.) are available from nearly every well. Attempts to correlate core permeability to porosity and/or other conventional logs using mathematical/statistical tools date back to the early '60's. Since then, regression analysis has been the most widely used approach for permeability prediction: this approach assumes that the permeability vs. porosity - or, alternatively, vs. conventional logs - functional relationships be known in advance. As a matter of fact, functional relationships are unknown.
In the last years, non-parametric regression techniques have become more and more widespread in the E&P industry. Unlike conventional regression algorithms, such techniques do not make a-priori assumptions with respect to the functional relationships amongst the investigated variables. Artificial Neural Networks (ANNs)1-8 and Alternating Conditioning Expectation (ACE) algorithms9 both belong to this category. Both methodologies are suitable for generating synthetic permeability curves, even though the permeability data type provided as input significantly impacts on the final result. Tests on actual reservoirs have shown that estimated permeability will be very close to actual data if continuous permeability curves from Nuclear Magnetic Resonance logging tools (KNMR) are used as input; conversely, when horizontal core plug permeabilities are used as input, the quality of the estimated permeability will be poorer than in the original core data and/or actual KNMR curves10.
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