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Glauconite develops widely in T Member in Oriente Basin, Ecuador. The presence of Galuconite reveals the information that can help to determine the sediment environment. However, the distribution of glauconite also makes the pay zone identification of glauconitic sandstone difficult in oilfield operation in many ways. Detailed core data analysis of glauconitic sandstone, include core description, thin section observation, porosity and water saturation, has been performed. The core data indicates that sediment environment changed from coastal estuary to open shelf during the deposition of T Member. The PEF and RHOB data from conventional well logs have been selected to estimate the concentration of glauconite based on the correlation with observation of thin section of core. And porosity estimation can be improved by using various matrix density with consideration of concentration of glauconite, which shows better result than using regular uniform matrix density by comparison with core data. Further, water saturation of glauconitic sandstone can be improved. And gas log data from mud logging has been introduced in pay zone identification of glauconitic sandstone. The combination analysis of well log data and gas log data can improve the determination of pay zone of glauconitic sandstone. The practice in oilfield proves the effectiveness of this method.

Prediction of lithology distribution is critical for development of oilfield and is also one of the basic tasks of geological modeling. Stochastic simulation has been proved to be an effective method for simulation the distribution of lithology. While stochastic simulation technique is theoretically a mathematical method, the result of simulation will be strongly affected by many factors, such as the quantity, quality, and distribution of well data, and so on. In the area or oilfield with limited wells and insufficient data, the uncertainty of simulation result will be very high, and the reliability of the result will be very low. This result with high uncertainty and low reliability will lead to very poor prediction of sand and shale. Further, the reliability of the result of petrophysical modeling will be low. This article introduces a new method by using constraint condition with high quality to improve the simulation result of lithology distribution and decrease the uncertainty of simulation result. Stratigraphic forward modeling is an effective approach to simulate the process of deposition, and this approach can characterize the geometry and distribution of complex sedimentation. But the resolution of the simulation result of this approach is lower than the requirement of development stage of oilfield. Seismic inversion has been proved to be an effective way with high resolution to describe the distribution of lithology by using high-quality seismic data, while seismic inversion is difficult to reflect the geological process and pattern. The combination of these two methods can provide good constraint condition for stochastic simulation. By using the results of stratigraphic forward modeling and seismic inversion, we can build the trend models for different lithology. The trend models can be used in stochastic simulation, and the trend models can improve the result of stochastic simulation. This paper took A oilfield in Oriente Basin in Ecuador as an example to show the process of this method. We built the stratigraphic forward modeling model based on the geological understanding and completed the research of seismic inversion of M1 layer of Napo Formation. The result of serigraphic forward modeling and seismic inversion was resampled into the geological model. Based on these two results, the trend models of sand and shale of M1 layer have been completed. The trend models have been used to constrain the stochastic simulation of lithology with sequential indicator simulation algorithm. This method can effectively decrease the uncertainty of simulation result caused by insufficient well data and improve the reliability of lithological model which further improves the result of petrophysical modeling.

Stochastic simulation of lithology is critical in geological modeling, and the result quality relies on the well data analysis. The traditional method may involve too many uncertainties due to the limited wells. This paper introduces a new method to improve the simulation of lithology distribution of tide-dominated estuary using probability volume generated by stratigraphic forward modeling as key input for trend model. Based on the core analysis and investigation on regional geology, the stratigraphic forward modeling approach generates an initial model by using the sequence stratigraphic scheme. And the sensitivity analysis provides an indication of adjusting the influencing parameters which control the sand and shale distribution. The models will be compared with the well data, geological concept, and seismic attribute. The selected model will be resampled into geological grid to generate the trend volume combined with seismic inversion data. Further, the lithology distribution can be simulated by using stochastic method with the trend volume. This approach has been successfully applied in JE-AW oil field to improve the geological model of M1. Typical tidal sedimentary structures, such as mud drapes, and wavy bedding shown in core and overall upward fining shown in logs, reveal the tide-dominated estuary environment during the deposition of M1. Three sub-zones (layers) of M1 are identified and correlated. Based on the sensitivity analysis, the sediment input and subsidence is adjusted for reliable stratigraphic forward modeling. The trend model is generated by inputting the result from stratigraphic forward modeling and seismic inversion. Finally, the lithology distribution is simulated using the trend model. This method improves the lithology stochastic simulation of tide-dominated estuary honoring the well and seismic data. This method reduces the uncertainties of stochastic modeling caused by limited wells and improves the predictability of lithology model.