Songxia Liu

Oil and gas reservoirs in volcanic rocks are a particular type of unconventional reservoir and present unique challenges for exploration and production engineers. To help the oil industry understand volcanic reservoirs and solutions to complex development problems, we reviewed their key engineering technologies as well as their geological characteristics. The distinctive geological characteristics of volcanic hydrocarbon reservoirs are strong heterogeneity, low porosity and permeability, complex fracture systems, etc. The volcanic reservoir rock types in order of hydrocarbon abundance are basalt (38.5%), andesite (15.9%), volcaniclastic (12.1%), and rhyolite (11.5%). The porosity ranges from 0.1 to 70%, and permeability ranges from 0.0007 to 762 md. In some commercially developed volcanic reservoirs of China, the average porosity is 7.7–13%; the average permeability is 0.41–3.4 md. Engineers have applied a variety of adapted technologies to produce volcanic reservoir economically. Horizontal wells can increase production and reserves by 4–6 times those of vertical wells, and longer wells are preferred. Specialized hydraulic fracturing techniques are suggested, including small or mixed proppant size, second HF treatment after proppant slugging, high-viscosity frac fluid with high-temperature resistance, special fluid loss reducer, high pump pressure, Extreme Overbalance Perforating, limited-entry fracturing, matrix acidizing, etc. Water control measures include producing below critical rates, partial perforation or penetration, controlling hydraulic fracture height, using horizontal wells, implementing complete cementing job, etc. Well productivity evaluation should be conducted to understand well performance and appropriately allocate production rates among wells, using the modified AOF method and other productivity prediction models considering breakdown fracture gradient, gas slippage effect, non-Darcy effect, etc. Well sites need to be selected based on recognizing profitable lithologies, lithofacies, high porosity and permeability, relatively developed fracture systems, thick net pay zones, etc. The critical questions for the industry are how to enhance volcanic reservoir recovery with more efficient and economic hydraulic fracturing and water control techniques. This is one of the first papers systematically summarizing the engineering technologies and unique solutions to develop volcanic reservoirs. Further and more complete reviews can be carried out in the future, and more novel and effective techniques can be explored and tested in the field.

Lufeng oil field was discovered in the 1980s in the Pearl River Mouth Basin (PRMB) of the South China Sea and has entered the stage of secondary oil recovery. One major problem that has restricted the subsequent exploration of the oil field is the unclear regional reservoir and caprock distribution, because practitioners have been using post-stack attributes and acoustic impedance inversion to analyze the distribution of sandstone (reservoir) and mudstone (caprock). In current geophysical research on reservoirs and caprocks, prestack inversion has been widely used because of its advantage over post-stack inversion. However, the accuracy of using single P/S-wave velocity ratio (V_P/V_S) or density (V_den) inverted by prestack to predict lithology is still insufficient. In this study, we created a new attribute V_RDEN, the sum of V_P/V_S and the weighted V_den, to capture the lithology variation of the reservoir. We integrated 3D seismic data and well log data and applied simultaneous prestack inversion and multiattribute regression analysis to determine reservoir properties, such as sand thickness, effective porosity, and distribution of sandstone and mudstone of the Lufeng oil field. Then, we calculated the new attribute V_RDEN from V_P/V_S and V_den obtained from simultaneous prestack inversion to determine the lithology variation. The multiattribute regression analysis, combining prestack attributes and post-stack attributes, indicates the effective porosity and sand volume in the Enping Formation, which contains the main oil-bearing reservoirs in the Lufeng oil field. Results show that when the sandstone thickness is greater than 12.5 m, the prediction error of V_RDEN is the lowest compared with V_P/V_S and V_den. In En-2 member of the Lower Enping Formation, medium- to high-porosity (14 to 17%) sandstone (19.5 m thickness) is widely distributed in the west and middle of the study area, with an area of nearly 300 km². The high-porosity sand zones stretch from the east of the Lower Enping Formation to the west of the Upper Enping Formation, which is the result of westward progradation of the braided delta. Our workflow used a novel seismic attribute V_RDEN in the simultaneous prestack inversion and multiattribute regression process to provide a more predictive spatial distribution of reservoir-nonreservoir features. The improved reservoir understanding will allow more efficient exploitation of the Lufeng oil field, and the improved workflow will facilitate exploration of other oil fields in the world.