Recognition and evaluation of low-resistivity pay

Abstract

A systematic procedure for the petrophysical identification and interpretation of low-resistivity and low-resistivity-contrast pay zones in intergranular reservoirs is founded upon an analysis of case histories for different reservoir types in diverse areas of the world. The approach acknowledges that a reservoir rock is a coupled physico-chemical system. The proposed method is generic and robust, it is conceptually simple, and it is structured in a manner that is easy to understand. The scheme is modular and it is arranged hierarchically to reflect maturing data scenarios: therefore, it can be progressively refined during the appraisal and development stages. The essence of the method is the definition and calibration of reliable interpretative procedures through quality-assured reference data from key wells by admitting only validated reservoir characteristics. Examples world-wide illustrate how failure to recognize low-resistivity pay can result in much loss of potential value. A principal thrust is to facilitate the re-evaluation of other wells within the same reservoir system without the need for excessive acquisition of additional data. However, the proposed interpretation framework does allow the incorporation of new logging technology as this becomes established. The end-product is a flexible petrophysical interpretation scheme for these unconventional reservoirs that benefits from cost-effectiveness, portability, a higher degree of exactness and consequently a much reduced uncertainty.

On the physics of frequency-domain controlled source electromagnetics in shallow water. 1: isotropic conductivity

“…The results in this paper pertain to a 10:1 resistivity contrast. Lower contrasts are frequently observed, and can even be reversed in shaly sands or carbonate host formations (Worthington 2000), while contrasts of 100:1 are quite rare. In the presence of a higher resistivity contrast, the effect of the galvanic reservoir will be stronger for a given offset and water depth, and hence the transitions noted regarding the Poynting vector, Joule heating and electric field will move to longer offsets.…”

DC conductivity, cationic exchange capacity, and specific surface area related to chemical composition of pore lining chlorites

“…Nevertheless, the protonic motion which is assumed to be responsible for the very poor conductivity observed in brucite type samples as Ni(OH) 2 and Mg(OH) 2 leads to higher activation energies of about 1 eV [30,31]. It is then realistic to assume that the dc conductivity of our clay samples is likely to result from the displacement of the cations located at the surface of the grain, i.e., the exchangeable cations.…”

“…Moreover, the possible effect of temperature on these properties has not been reported yet. According to Worthington [2], low resistivity in argillaceous sandstone reservoirs may be attributed either to the effect of microporosity or to specific effects due to clays and/or to conducting minerals. Effect of clays' CEC, such as smectite, illite, and kaolinite, on resistivity measurements of sandstones has been widely studied in the literature and has contributed in the building of well-known log interpretation models [3][4][5][6][7].…”

Calculation of water saturation in low resistivity gas reservoirs and pay-zones of the Cretaceous Grudja Formation, onshore Mozambique basin

“…Low-resistivity pay zones display a low-resistivity contrast between a brine-water-bearing section of the reservoir and a hydrocarbon-bearing (oil or gas) part of the reservoir (Worthington, 2000). Hamada et al (2000), subdivided the cause of low-resistivity pay zones into two main categories:…”

“…The causes of low-resistivity are directed at specific reservoirs or particular depositional environments and may not be directly applicable to other reservoirs, where conditions initially appear to be similar (Worthington, 2000). It is therefore necessary to study and understand the specific causes of the low-resistivity pay in the J-and K-Reservoirs of the Mozambique basin and to recommend an algorithm and a workflow that can improve the accuracy of calculation of water saturation in these reservoirs.…”