Novel method for the rapid evaluation of pressure depletion in tight oil reservoirs

Chao Ding, Jin Chen, Guo Yang, Runfei Bao, Yan Dou, Kaoping Song

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Abstract


Tight oil reservoirs hold immense development potential but are characterized by challenging reservoir properties, severe heterogeneity, and extremely low permeability and porosity. Massive hydraulic fracturing of horizontal wells is applied to achieve sustainable production in these reservoirs. The swift assessment of pressure depletion in tight reservoirs is essential for their successful and cost-effective development. Traditional pressure testing methods necessitate well shutdown, impacting subsequent production, while numerical simulation methods demand significant computational resources and expertise from technical personnel. To identify the sensitivity parameters influencing the reservoir pressure drop, this study uses a Plackett-Burman design and variance analysis. Using numerical simulations, variance analysis and multi-linear regression, we formulate evaluation indices and surrogate models for individual well depletion. The method’s reliability is validated through multiple experiments along with testing data. Our rapid evaluation method accurately assesses pressure depletion in typical well groups, with a fitting rate exceeding 85%. In regions where the pressure maintenance is below 80%, indicating severe reservoir depletion, enhanced oil recovery treatments, e.g., gas or water injection, are applied based on the evaluation results. The proposed method for evaluating individual well pressure depletions provides crucial guidance for realizing the efficient development of tight oil reservoirs.

Document Type: Short communication

Cited as: Ding, C., Chen, J., Yang, G., Bao, R., Dou, Y., Song, K. Novel method for the rapid evaluation of pressure depletion in tight oil reservoirs. Advances in Geo-Energy Research, 2024, 11(1): 74-80. https://doi.org/10.46690/ager.2024.01.07


Keywords


Tight oil reservoirs, depletion-driven development, reservoir pressure depletion assessment, surrogate models linear regression

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References


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DOI: https://doi.org/10.46690/ager.2024.01.07

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