Integrated Evaluation of Laminated Sand-Shale Gas-Bearing Reservoir Using Tensor Model: A Case Study Combining Data from Triaxial Resistivity, Image, Sonic, and Reservoir Testing in B-Field, Malaysia

Wijaya, Aditya Arie (Halliburton) | Wu, Ivan Zhia Ming (Halliburton) | Parashar, Sarvagya (Halliburton) | Iffwad, Mohammad (Halliburton) | Yaakob, Amirul Afiq B (Petronas Carigali) | Tolioe, William Amelio (Petronas Carigali) | Sidid, Adib Akmal Che (Petronas Carigali) | Ahmad, Nadhirah Bt. (Petronas Carigali)

OnePetro 

Abstract In recent years, the development of frontier areas brings added challenges to formation evaluation, especially thinly bedded reservoirs. It is challenging to evaluate such reservoirs due to the low resistivity values and high shale volume, which masks the contrast between water and hydrocarbon zones. Using conventional approaches in these types of reservoirs will underestimate the hydrocarbon potential and reserves estimates. A study has been carried out of the thin-bed laminated reservoir in B-field using the tensor model technique to assess the hydrocarbon potential. Additional data from borehole imaging and sonic logs are critical for enhancing the evaluation of hydrocarbon potential and complements the result of the tensor model evaluation. The study was conducted to calculate the sand resistivity and sand porosity using a combination of the tensor model and the Thomas-Stieber model. The tensor model uses acquired horizontal and vertical resistivities, while the Thomas-Stieber model uses the calculated shale volume and porosity. One of the main parameters in the tensor model is shale resistivity, which upon analysis, varies across many shale sections in the well. This uncertainty is reduced by picking multiple shale resistivity values based on borehole image facies analysis. The VPVS ratio technique and Brie’s plot using compressional and shear travel time are used as a qualitative analysis that indicates the same gas-bearing interval. The tensor model calculations improve hydrocarbon saturation by a range of 4-21%, depending on sand thickness and shale volume, which increases the net to gross by more than 20%. The borehole image facies analysis helps to objectively pick the shale resistivity parameters to avoid subjective interpretation and underestimating the pay. A qualitative approach using sonic data helps to identify the potential gas-bearing interval and complement the previous tensor model interpretation. Although all interpretation methods indicate a similar gas-bearing interval that correlates with the mudlog total gas reading, the combination of the tensor and Thomas-Stieber method with image constrained shale resistivities gives the most definitive gas saturation and net pay The novelty of this study is to showcase two things. First is the application of combined tensor and Thomas-Stieber model in a laminated reservoir, with image constrained shale resistivity for improved gas saturation and net pay. The second is to highlight the use of gas-sensitive sonic data to confirm the gas saturated interval.

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