| The reef flat is a special kind of carbonate sediments which possesses unique pore space, with a generally high porosity and permeability, and it is an excellent reservoir of carbonate rock, blessed with a great potential for hydrocarbon. The reef flat facies reservoir is widely distributed in the world, as well as my country, and is abundant in gas and oil. However, the prediction of reef flat facies reservoir is always a difficult thing in hydrocarbon exploration and most of wells in which the reef is found by accident.In this study, taking SYB regions Changxing formation reef flat facies reservoir predication as an example, we formed a technical system of reef flat facies reservoir integrated predication, based on reef flat facies reservoir predication difficult, characteristics of seismic response and log response of reef flat facies reservoir. First of all, we detailed introduced the concept and classification of the reef and flat, and summed up the characteristics of seismic response and logging response patterns, sedimentary facies patterns, sedimentary environment, sedimentary feature, and reservoir characteristic. Secondly, we carefully depicted the location of the study area, tectonic development history, structural feature, sedimentation patterns, as well as stratigraphic features, and analyzed the study area geology, drilling, logging data, statistics of logging features of the physical parameters. Logging parameters statistics showed that black-gray muddy limestone has low-speed, low-density, low acoustic impedance characteristics, which is the same as the conventional understanding of the reservoir characteristics. We also finished seismic horizon calibration and fine interpretation. Thirdly, we put forward to predict the reef flat facies reservoir's clue and method, based on resuming palaeogeomorphology, seismic reflection structural feature, waveform complexity(dimension of relevance and approximate entropy), lateral discontinuity(coherency cub technology and local structural entropy), and waveform classification. The results showed that it is in favor of determining the location of reef flat development to use palaeogeomorphic analytic technique, combining the development environment of reef flat, and it is an important means to use seismic reflection structural feature, but this method has intense subjectivity, and demand to identify the abnormal scope of the reef flat over the three-dimensional seismic profiling one by one. With a view to overall seismic waveform changes, we applied coherent technology, local structural entropy analysis, fractal dimension and approximate entropy by calculating the seismic waveform complexity and coherence to directly identify and predict the distribution of reef-flat. The actual data processing and analysis proved that the application of coherent technology and local structural entropy analysis detect the degree of discontinuity to distinguish between reef-flat formation and non-reef-flat formation. Fractal dimension and approximate entropy attributes can portray sophisticated the distribution of reef-flat. Fractal dimension attribute, moreover, more precise to delineate the distribution of reef-flat than approximate entropy to do. Waveform classification techniques based on neural network form the waveform characteristics of the overall changes can automatically classify to seismic waveform,which can delineate accurately seismic facies,and to speed up the progress of the interpretation. Characteristics of integration of the above mention technologies can be identified the scope of Changxing Formation reef flat facies distribution, which provide the necessary premise, and establish a solid foundation for reservoir distribution in next step. Fourthly, we applied attribute analysis, acoustic impedance inversion, time-frequency analysis and frequency attenuation attribute to predict the distribution of Changxing formation reef flat facies reservoir in the foregoing scope. Forecast results showed that the strong-amplitude within the weak-amplitude indicated the existence of reservoirs in the predictive scope of reef flat distribution in the fourth chapter. Reservoir behaved the high absorption, stronger amplitude and low-frequency features etc. Because of the impedance differences between overburden and underlying formation, acoustic impedance profile can be identify top and bottom interface. However, it is difficult in using impedance attribute to characterize lateral change of reservoir, because of velocity contrasts between reef flat reservoir and adjacent rock. Whereas, single-frequency profile is calculated by trace to trace in time-frequency analysis so that it is not going to change to identify the lateral resolution of reservoir, and what's more, low-frequency energy increased, and high-frequency energy rapidly decreased, so, the difference between top and bottom low-frequency energy and high-frequency energy of the reservoir can determine the reservoir's lateral distribution and border. Therefore, combining time-frequency analysis technology with wave impedance inversion is able to delineate subtly distribution of the reef flat facies reservoir in both horizontal and vertical, and location and rang of hydrocarbon zone, and to help to calculate the thickness of oil and gas and gas reserves, to reduce the drilling rate of failure. The attenuation gradient attributes which derived from the time-frequency analysis, can be used to effectively guide the distribution of reef flat reservoir. But, we should analysis the factors that affect it, and determine the most critical factor that lead to frequency attenuation, from logging and the refined geological context. The factors that impact the frequency attenuation are the lithology (black muddy limestone) and the fluid and fluid saturation in porosity in SYB region Changxing formation. The forecasting results of a variety of methods showed reservoir is better and wider in flat facies area than in reef facies. Fifth, we increased the parameters of seismic attributes that indicate reservoir quality in order to make reservoir predictive study tend to a complete, but only the increase in the number of attributes, we by any possibility increased the complexity of reservoir prediction. The study results showed that certain relevance consist in during the various attributes, we, therefore, presents the use of factor analysis and cluster analysis for integrated reservoir forecasting. The lesser parameters that substituted for the original multi-attribute, are able to reflect all of the original information, and can be achieved good results in the application of reservoir prediction. However, the disadvantages of the results of both methods are only qualitative analysis and to need to use the drilling calibrated results. Finally, we use probabilistic neural network in Strata software module EMERGE carried out the transformation from multi-attribute to porosity parameters, so that we can achieve the quantitative prediction of reservoir.To sum up, using the above methods can better predict the scope of the distribution of reef flat, and the scope of the favorable reservoir area, accordingly, to provide evidence for the deployment of drilling, and to reduce the error rate of drilling. |
