What are the pros and cons of porosity and permeability estimation methods?

Core poro-perm measurement shall give us the overall picture of the relationships and the expected trends between those parameters, which are not necessarily accurate without link them to Sw_log vs HAFWL parameters. So, since the core permeabilities, which are representing usually between 5-25% of the measured data while the remaining permeabilities are predicted i.e. (one of the biggest unknown that are the most sensitive and effective parameter impacting dynamic models History Match), then by readjustment block grid cells permeabilities will be the 1st attractive element and the easiest action for RE’s to modify them to achieve good History Match.

The only problem with this is the conditions where this core sample has been brought to the surface versus the conditions where the testing has been done. Also, duration of time between actual conditions and laboratory conditions at test times matters. Above all sample collection and resolver representation, will lead to assuming as homogenous as the entire reservoir would be.

have you included other sources to estimate K? like magnetic resonance or formation testers. this last one when is configured for vertical interference test additional to Kh can provide the Kv/Kh, the k anisotropy.

Most commonly, to estimate permeability, we can use values of porosity, pore size distribution, and water saturation from logging data and established correlations. One benefit of using wireline log data to estimate permeability is that it can provide a continuous permeability profile throughout a particular interval.

Core analysis is not time consuming & not expensive. A laboratory can be setup on the rig platform, to analyse the drill cuttings. The type of the laboratory analysis/chemicals use will depend on the type of the drilling fluid (mud) used to drill, water-based- mud or oil-based-mud.

Los registros al ser medidas indirectas, se deben calibrar con los análisis de núcleos, llevados a profundidad. En yacimientos altamente heterogéneos lo ideal es identificar las unidades de flujo y evaluarlas por separados para tener estimaciones más acertadas de porosidad y permeabilidad.

Saturation-height functions are the main impediment to making this important connection. By their very name, they say the saturation depends on height, thereby relegating permeability to secondary consideration, if at all. Indeed, some saturation-height modellers wrongly believing that decoupling permeability from saturation modelling is an advantage of their methodology. In fact, it prevents them from appreciating how we can learn about permeability through saturation.

Since Sw and Porosity logs, which are the most measured profiles across all the wells, then those parameters should be considered as the last option needs to be adjusted and tuned in order to build better correlations between poro-perm vs RRT’s and HAFWL’s vs Sw_log profiles. Converting core plugs permeability into log permeability profiles might be representing another key challenging and uncertainty, which is requiring to averaging few scattered permeabilities values into single value per the blocked grid cells that’s have several feet’s in thickness.

Well logging is a bit expensive, providing the equipments & Technical crew. Most times it can only be affordable by big multinationals e.g Baker Hughes, Siemens, General Electric, Schlumberger, Weatherford, Halliburton & their likes.

Density logging involves measuring the bulk density of the formation. Porosity can be estimated from the difference between the bulk density of the formation and the density of the matrix material. This method is particularly useful in formations where the matrix density is relatively constant. Neutron logging measures the hydrogen content of the formation. Since hydrocarbons contain hydrogen, neutron logging can indirectly estimate porosity by measuring the hydrogen index. This method is commonly used in hydrocarbon-bearing formations.While direct measurement of permeability through well logging is challenging, permeability can be indirectly estimated from other well log measurements such as porosity, grain size, and lithology.

By the time we are trying to estimate the parameters through well testing (i.e dynamic parameters) we would have already obtained the static parameters through core flood/ well logging. Most of the time, well testing give us a sanctity of the obtained parameters atleast near to it or with certain degree of difference provided the uncertainities involved during carrying out the study. However for unconsolidated formations as I work on, they play essential role in stating the stress based permeabilities with time and production. So, many of the objectives get solved as whole.

Introduction. Production well testing refers to the process of performing periodic measurements of well productivity. Main parameters to be measured are flow rates (oil, gas, and water), pressures and temperatures (downhole and surface), fluid properties (density, shrinkage, SG, composition, etc.)

Well testing is the dynamic measurement of porosity and permeability using Darcy’s law and the pressure and flow rate data from production. Data quality is one of its cons. Under the same test scenarios, these parameters might differ and in some cases, the tests aren’t conducted for 24hrs. So what ever result gotten as the daily production rate might be underestimated or overestimated.

Pros: Dynamic Data: Provides information on the dynamic behavior of the reservoir, including flow rates and pressure responses. Field-Wide Insights: This can offer insights into the reservoir's permeability and heterogeneity at a larger scale than core analysis or logging. Validation Tool. Cons: Indirect Estimation: Like thriving logging, it provides indirect estimates of porosity and permeability that rely on interpreting pressure and flow data. Operational Complexity: Requires sophisticated equipment and operations, including managing produced fluids. Temporal and Spatial Limitations: The data represent the reservoir's properties during the test and may not capture changes or variations across different reservoir zones over time.

Seismic inversion is a good tool. This especially useful for rock prediction / reservoir properties prediction prior to well drilling. The result is as good as the available data, the more data used to build the algorithm and the good understanding of the geology of the environment, the better the prediction. Processing sequence and quality of the seismic data is equally very key in the predictive analysis.

Seismic inversion methods are routinely used to estimate attributes like P-impedance, S-impedance, density, the ratio of P-wave and S-wave velocities and elastic impedances from seismic and well log data. These attributes help to understand lithology and fluid contents in the subsurface.

As Felix Olalekan Oluyemi said, we can use seismic inversion attributes to do the job, but going further, we can use seismic inversion and conventional attributes to get porosity and WS and then use the same seismic attributes together with porosity and WS previously generated to get a high nonlinear regression that allows us to obtain the permeability, Until now for us it has been the method that brought us to the best solution.

Machine learning can be utilized to identify lateral/vertical connections (pressure cells) in-between different structures in the reservoir through training model by seismic data and logs to get porosity and permeability cubes sensitivities in areas not penetrated before, and hence we can detect areas requiring more data (samples, cores, Wireline logs) and areas where analogue vicinity wells can be used for this data requirement.

Machine learning method can provide a very good estimation of porosity and permeability for unit, layer, formation, and full field. The way machine learning works is very unique various regression models can be implemented to calculate porosity and permeability (e.g Models Like : XGBoost, DecisionTree, MultiLinear Regression, Etc).All of those methods can have as many inputs (independed variables) as the user wishes, even the values derived from core plugs can be implemented as inputs for those models.

Lo siento Oscar, cometí un error. No es un problema subestimación, lo que obtenemos es una solución suavizada, que no capta los valores extremos or outlayers.

One of the notable failings of permeability estimation methods is they ignore the connection with water saturation. Permeability and saturation are related through J Functions, which stem from dimensional analysis. Despite including permeability, J Functions are very well-behaved, unlike typically noisy poro-perm relationships. This implies saturation can be used to constrain permeability estimates through log-derived J Functions. For example, when water saturation is 100% above a contact, it implies that permeability ought to be low. Any permeability estimation based on porosity and rock type that fails to be consistent with saturation makes no physical sense.

Most common method of petrophysical evaluation is the mixture of all methodologies presented above, however this can be subjected to the cost and overall CAPEX/OPEX balances..

En términos generales, se puede inferir lo siguiente: - Pros: se tiene un estimado del valor de Phi y K. - Contras: el nivel de incertidumbre asociado al valor obtenido de Phi y K según el método aplicado. - Sugerencia: aplicar el análisis geostadistico para obtener datos con mayor certidumbre, siendo el valor más certero el arrojado por el núcleo.

Irrespective of the pros & cons of individual measurements like poro & perm, it's the Sw which is the link which weaves a coherent & meaningful story via the J function - viewing each element in isolation is misleading. @Dan O'Meara has elegantly explained this in great detail in his many posts on the subject.

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