As one of the subtle reservoirs, low-resistivity-low-contrast (LRLC) pay zones are crucial potential exploration objective in Ordos basin. However, since its resistivity similarity to the adjacent water zones, and the genetic mechanism is complex, thence, LRLC pay zones still produce hydrocarbon at minimum resistivity contrast between hydrocarbon-bearing intervals and water-wet or shaly zones. So, if LRLC pay zones could be accurately identified only by conventional logging curves, it would bring new reserves to the development of Yanchang Oilfield.

Focusing on the difficulties in well logging identification of Chang 2 LRLC pay zones in Zhidan area of Ordos basin, the work on logging identification of low resistivity pay zones in this area is carried out by processing field data such as drilling coring, well logging curves, oil testing and daily production data. Meanwhile, combined with the experimental data such as NMR experiments, rock electrical experiments, laser particle size and cation exchange capacity experiments, we form an integrated workflow based on petrography, rock typing and petrophysical methods, and deal with the identification, characterization and evaluation of LRLC pay zones.

This study indicates that under the deposition environment of delta plain subfacies, Chang 2 reservoir is dominated by medium-fine-grained feldspar sandstone, and the pore structure is extremely complex due to the strong compaction. Therefore, the key cause for LRLC pay zones is the high salinity of formation water, accompanied by secondary reasons such as complex pore structure, and additional electron conductivity of the clay. In order to effectively identify the pay zones, we establish a set of suitable logging curve interpretation models based on the "four properties" relationship and test them with oil testing data, which could improve the accuracy of these models. Finally, the "apparent formation water resistivity - deep induced resistivity" cross-plot, the adjacent water zone comparison and the multivariate discriminant methods are selected to be suitable for Chang 2 low resistivity pay zones in the area. And these methods could help engineers to better estimation of water saturation in the low resistivity pay zones and accurately determine the target layer by using only limited set of well log data (conventional well logging data).

In this work, three effective logging identification methods have been proposed to determine the advantaged pay zones from qualitative or quantitative perspectives. Through real block verification, these methods could effectively improve the coincidence rate of logging identification, and would provide bases for selecting the target layers in original development areas. More importantly, the results may offer new perspectives for risk assessment and target layer determination of other similar low resistivity reservoirs exploration and development.

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