Yuming Liu

Characterization of Architectural Elements of Ordovician Fractured-cavernous Carbonate Reservoirs, Tahe Oilfield, China

A new method for characterizing architectural elements of fractured-cavernous carbonate reservoirs was proposed, with application to an Ordovician reservoir in the Tahe Oilfield, Tarim basin. The new method integrates observations from outcrop analogs and concepts from modern karst theory for the mapping and modeling of fractured-cavernous reservoirs. In this paper, fractured-cavernous reservoirs were divided into 4 architectural element types—underground river caverns, sinkholes, discrete internal caverns, and faults. Architectural elements of the Ordovician reservoir in Tahe oilfield were identified and characterized by integrating well logs and seismic data. A new method constrained by faults, karst zones, and seismic acoustic impedance data, was introduced to build a 3D model of architectural elements of fractured-cavernous reservoir in the S48 unit of Tahe Oilfield. A porosity model was then derived from the architectural element model using facies-constrained method. The research provides a work-flow for the characterization of fracturedcavernous reservoirs and determining optimal methods for maximizing oil recovery in the study area or in similar areas.

Impacts of water flooding on pore structure of sandstone reservoirs—case study of Wang Guantun oilfield, Bohai Bay Basin, China

To study the variation mechanism of pore structure of sandstone reservoir in different stages of water flooding, water flooding experiment and a series of parallel tests are performed on typical continental sandstone samples from the Bohai Bay Basin of China, including thin sections (TS), scanning electron microscopy (SEM), X-ray diffraction (XRD), rate-controlled mercury injection (RMI), and X-ray computer tomography (CT). SEM results showed that three pore types existed in sandstone reservoir, including inter-granular pores, dissolution pores, and pores within clay aggregates. By comparing the mineral composition and pore throat characteristics of samples, we observed the variation characteristics of pore structure in different stages of water flooding are different, and the potential factors that affect these variations mainly were the original pore structure of rocks and the content of swelling-intend mineral. For the reservoir with larger pore size and more smectite, pore spaces were reduced, and pore throats were blocked by the clay swelling and particle migration in the early stage of water flooding. As water injection volume increases, the injected water plays a major role to wash tiny particles away, which results in pore spaces increasing and some closed pores re-open. For the reservoir with smaller pore size and less smectite, the dominant mechanism in the early stage of water flooding was pore space cleaning—tiny particle was washed away from the pore path, which results in pore size increasing. As water injection volume increases, clay swelling reaction becomes to be the critical factor, reducing the pore size. This study makes clear the variation regularity and the variation mechanism of pore structure of continental sandstone reservoir in different stages of water flooding based on the combination of CT scan and water flooding experiment.

Application of artificial neural network in Geology: Porosity estimation and lithological facies classification

Based on the relationship between porosity (or lithological facies) and other petrophysical properties, Artificial neural networks (ANN) are respectively trained for porosity estimation and lithological facies classification, using core porosity (CPOR) data and core lithological facies interpretation results of part of core interval together with some well logs (petrophysical properties). After the ANN were constructed, they were used for porosity estimation and lithological facies identification in both trained and untrained core intervals, for further analysis of errors or accuracies of the estimated results and ANN. After careful analysis, the error of estimated porosity is from -0.3 to 0.3, and the accuracy of lithological facies identification is 0.7, both showing high reliability of ANN. The constructed ANN can be confidently applied for other un-cored wells or intervals.