Integrated Reservoir Prediction and Oil-Gas Evaluation in the Maoshan Area

The Maoshan area is an area with well-developed igneous rocks and complex structures. The thickness of the reservoirs is generally small. The study of the reservoirs is based on seismic data, logging data and geological data. Using techniques and software such as Voxelgeo, BCI, RM, DFM and AP, the authors have made a comprehensive analysis of the lateral variation of reservoir parameters in the Upper Shazu bed of the third member of the Palaeogene Funing Formation, and compiled the thickness map of the Shazu bed. Also, with the data from ANN, BCI and the abstracting method for seismic characteristic parameters in combination with the structural factors, the authors have tried the multi-parameter and multi-method prediction of petroleum, delineated the potential oil and gas areas and proposed two well sites. The prediction of oil and gas for Well JB2 turns out to be quite successful.

A transient production prediction method for tight condensate gas wells with multiphase flow

Considering the phase behaviors in condensate gas reservoirs and the oil-gas two-phase linear flow and boundary-dominated flow in the reservoir,a method for predicting the relationship between oil saturation and pressure in the full-path of tight condensate gas well is proposed,and a model for predicting the transient production from tight condensate gas wells with multiphase flow is established.The research indicates that the relationship curve between condensate oil saturation and pressure is crucial for calculating the pseudo-pressure.In the early stage of production or in areas far from the wellbore with high reservoir pressure,the condensate oil saturation can be calculated using early-stage production dynamic data through material balance models.In the late stage of production or in areas close to the wellbore with low reservoir pressure,the condensate oil saturation can be calculated using the data of constant composition expansion test.In the middle stages of production or when reservoir pressure is at an intermediate level,the data obtained from the previous two stages can be interpolated to form a complete full-path relationship curve between oil saturation and pressure.Through simulation and field application,the new method is verified to be reliable and practical.It can be applied for prediction of middle-stage and late-stage production of tight condensate gas wells and assessment of single-well recoverable reserves.

A systematic machine learning method for reservoir identification and production prediction

Reservoir identification and production prediction are two of the most important tasks in petroleum exploration and development.Machine learning(ML)methods are used for petroleum-related studies,but have not been applied to reservoir identification and production prediction based on reservoir identification.Production forecasting studies are typically based on overall reservoir thickness and lack accuracy when reservoirs contain a water or dry layer without oil production.In this paper,a systematic ML method was developed using classification models for reservoir identification,and regression models for production prediction.The production models are based on the reservoir identification results.To realize the reservoir identification,seven optimized ML methods were used:four typical single ML methods and three ensemble ML methods.These methods classify the reservoir into five types of layers:water,dry and three levels of oil(I oil layer,II oil layer,III oil layer).The validation and test results of these seven optimized ML methods suggest the three ensemble methods perform better than the four single ML methods in reservoir identification.The XGBoost produced the model with the highest accuracy;up to 99%.The effective thickness of I and II oil layers determined during the reservoir identification was fed into the models for predicting production.Effective thickness considers the distribution of the water and the oil resulting in a more reasonable production prediction compared to predictions based on the overall reservoir thickness.To validate the superiority of the ML methods,reference models using overall reservoir thickness were built for comparison.The models based on effective thickness outperformed the reference models in every evaluation metric.The prediction accuracy of the ML models using effective thickness were 10%higher than that of reference model.Without the personal error or data distortion existing in traditional methods,this novel system realizes rapid analysis of data while reducing the time required to resolve reservoir classification and production prediction challenges.The ML models using the effective thickness obtained from reservoir identification were more accurate when predicting oil production compared to previous studies which use overall reservoir thickness.

Pre-stack inversion for caved carbonate reservoir prediction:A case study from Tarim Basin,China

The major storage space types in the carbonate reservoir in the Ordovician in the TZ45 area are secondary dissolution caves.For the prediction of caved carbonate reservoir,post-stack methods are commonly used in the oilfield at present since pre-stack inversion is always limited by poor seismic data quality and insufficient logging data.In this paper,based on amplitude preserved seismic data processing and rock-physics analysis,pre-stack inversion is employed to predict the caved carbonate reservoir in TZ45 area by seriously controlling the quality of inversion procedures.These procedures mainly include angle-gather conversion,partial stack,wavelet estimation,low-frequency model building and inversion residual analysis.The amplitude-preserved data processing method can achieve high quality data based on the principle that they are very consistent with the synthetics.Besides,the foundation of pre-stack inversion and reservoir prediction criterion can be established by the connection between reservoir property and seismic reflection through rock-physics analysis.Finally,the inversion result is consistent with drilling wells in most cases.It is concluded that integrated with amplitude-preserved processing and rock-physics,pre-stack inversion can be effectively applied in the caved carbonate reservoir prediction.

Multiple-Element Matching Reservoir Formation and Quantitative Prediction of Favorable Areas in Superimposed Basins

Superimposed basins in West China have experienced multi-stage tectonic events and multicycle hydrocarbon reservoir formation, and complex hydrocarbon reservoirs have been discovered widely in basins of this kind. Most of the complex hydrocarbon reservoirs are characterized by relocation, scale re-construction, component variation and phase state transformation, and their distributions are very difficult to predict. Research shows that regional caprock （C）, high-quality sedimentary facies （Deposits, D）, paleohighs （Mountain, M） and source rock （S） are four geologic elements contributing to complex hydrocarbon reservoir formation and distribution of western superimposed basins. Longitudinal sequential combinations of the four elements control the strata of hydrocarbon reservoir formation, and planar superimpositions and combinations control the range of hydrocarbon reservoir and their simultaneous joint effects in geohistory determine the time of hydrocarbon reservoir formation. Multiple-element matching reservoir formation presents a basic mode of reservoir formation in superimposed basins, and we recommend it is expressed as T-CDMS. Based on the multiple-element matching reservoir formation mode, a comprehensive reservoir formation index （Tcdms） is developed in this paper to characterize reservoir formation conditions, and a method is presented to predict reservoir formation range and probability of occurrence in superimposed basins. Through application of new theory, methods and technology, the favorable reservoir formation range and probability of occurrence in the Ordovician target zone in Tarim Basin in four different reservoir formation periods are predicted. Results show that central Tarim, Yinmaili and Lunnan are the three most favorable regions where Ordovician oil and gas fields may have formed. The coincidence of prediction results with currently discovered hydrocarbon reservoirs reaches 97 %. This reflects the effectiveness and reliability of the new theory, methods and technology.

Displacement characteristics and prediction of Baishuihe landslide in the Three Gorges Reservoir

In order to reach the designated final water level of 175 m, there were three impoundment stages in the Three Gorges Reservoir, with water levels of 135 m, 156 m and 175 m. Baishuihe landslide in the Reservoir was chosen to analyze its displacement characteristics and displacement variability at the different stages. Based on monitoring data, the landslide displacement was mainly influenced by rainfall and drawdown of the reservoir water level. However, the magnitude of the rise and drawdown of the water level after the reservoir water level reached 175 m did not accelerate landslide displacement. The prediction of landslide displacement for active landslides is very important for landslide risk management. The time series of cumulative displacement was divided into a trend term and a periodic term using the Hodrick-Prescott(HP) filter method. The polynomial model was used to predict the trend term. The extreme learning machine(ELM) and least squares support vector machine(LS-SVM) were chosen to predict theperiodic term. In the prediction model for the periodic term, input variables based on the effects of rainfall and reservoir water level in landslide displacement were selected using grey relational analysis. Based on the results, the prediction precision of ELM is better than that of LS-SVM for predicting landslide displacement. The method for predicting landslide displacement could be applied by relevant authorities in making landslide emergency plans in the future.

Prediction of chlorophyll a concentration using HJ-1 satellite imagery for Xiangxi Bay in Three Gorges Reservoir

Since the impoundment of the Three Gorges Reservoir in 2003, algal blooms have frequently been observed in it. The chlorophyll a concentration is an important parameter for evaluating algal blooms. In this study, the chlorophyll a concentration in Xiangxi Bay, in the Three Gorges Reservoir, was predicted using HJ-1 satellite imagery. Several models were established based on a correlation analysis between in situ measurements of the chlorophyll a concentration and the values obtained from satellite images of the study area from January 2010 to December 2011. Chlorophyll a concentrations in Xiangxi Bay were predicted based on the established models. The results show that the maximum correlation is between the reflectance of the band combination of B4/（B2＋B3） and in situ measurements of chlorophyll a concentration. The root mean square errors of the predicted values using the linear and quadratic models are 18.49 mg/m3 and 18.52 mg/m3, respectively, and the average relative errors are 37.79% and 36.79%, respectively. The results provide a reference for water bloom prediction in typical tributaries of the Three Gorges Reservoir and contribute to large-scale remote sensing monitoring and water quality management.

Geochronological Records of Oil-Gas Accumulations in the Permian Reservoirs of the Northeastern Ordos Basin

Geochronology of oil-gas accumulation （OGA） is a challenging subject of petroleum geology in multi-cycle superimposed basins.By K-Ar dating of authigenic illite （AI） and fluid inclusion （FI） analysis combined with apatite fission track （AFT） thermal modeling,a case study of constraining the OGA times of the Permian reservoirs in northeast Ordos basin （NOB） has been conducted in this paper.AI dating of the Permian oil-gas-bearing sandstone core-samples shows a wide time domain of 178-108 Ma.The distribution of the AI ages presents 2-stage primary OGA processes in the Permian reservoirs,which developed in the time domains of 175-155 Ma and 145-115 Ma with 2-peak ages of 165 Ma and 130 Ma,respectively.The FI temperature peaks of the samples and their projected ages on the AFT thermal path not only present two groups with a low and a high peak temperatures in ranges of 90-78℃ and 125-118℃,respectively corresponding to 2-stage primary OGA processes of 162-153 Ma and 140-128 Ma in the Permian reservoirs,but also appear a medium temperature group with the peak of 98℃ in agreement with a secondary OGA process of c.～30 Ma in the Upper Permian reservoirs.The integrated analysis of the AI and FI ages and the tectono-thermal evolution reveals that the Permian reservoirs in the NOB experienced at least 2-stage primary OGA processes of 165-153 Ma and 140-128 Ma in agreement with the subsidence thermal process of the Mid-Early Jurassic and the tectono-thermal event of the Early Cretaceous.Then,the Upper Permian reservoirs further experienced at least 1-stage secondary OGA process of c.～30 Ma in coincidence with a critical tectonic conversion between the slow and the rapid uplift processes from the Late Cretaceous to Neogene.

Machine learning seismic reservoir prediction method based on virtual sample generation

Seismic reservoir prediction plays an important role in oil exploration and development.With the progress of artificial intelligence,many achievements have been made in machine learning seismic reservoir prediction.However,due to the factors such as economic cost,exploration maturity,and technical limitations,it is often difficult to obtain a large number of training samples for machine learning.In this case,the prediction accuracy cannot meet the requirements.To overcome this shortcoming,we develop a new machine learning reservoir prediction method based on virtual sample generation.In this method,the virtual samples,which are generated in a high-dimensional hypersphere space,are more consistent with the original data characteristics.Furthermore,at the stage of model building after virtual sample generation,virtual samples screening and model iterative optimization are used to eliminate noise samples and ensure the rationality of virtual samples.The proposed method has been applied to standard function data and real seismic data.The results show that this method can improve the prediction accuracy of machine learning significantly.

Prediction of Sedimentary Microfacies Distribution by Coupling Stochastic Modeling Method in Oil and Gas Energy Resource Exploitation

In view of the problem that a single modeling method cannot predict the distribution of microfacies, a new idea of coupling modeling method to comprehensively predict the distribution of sedimentary microfacies was proposed, breaking the tradition that different sedimentary microfacies used the same modeling method in the past. Because different sedimentary microfacies have different distribution characteristics and geometric shapes, it is more accurate to select different simulation methods for prediction. In this paper, the coupling modeling method was to establish the distribution of sedimentary microfacies with simple geometry through the point indicating process simulation, and then predict the microfacies with complex spatial distribution through the sequential indicator simulation method. Taking the DC block of Bohai basin as an example, a high-precision reservoir sedimentary microfacies model was established by the above coupling modeling method, and the model verification results showed that the sedimentary microfacies model had a high consistency with the underground. The coupling microfacies modeling method had higher accuracy and reliability than the traditional modeling method, which provided a new idea for the prediction of sedimentary microfacies.

Pre-Drilling Prediction Techniques on the High-Temperature High-Pressure Hydrocarbon Reservoirs Offshore Hainan Island,China

Decreasing the risks and geohazards associated with drilling engineering in high-temperature high-pressure(HTHP) geologic settings begins with the implementation of pre-drilling prediction techniques(PPTs). To improve the accuracy of geopressure prediction in HTHP hydrocarbon reservoirs offshore Hainan Island, we made a comprehensive summary of current PPTs to identify existing problems and challenges by analyzing the global distribution of HTHP hydrocarbon reservoirs, the research status of PPTs, and the geologic setting and its HTHP formation mechanism. Our research results indicate that the HTHP formation mechanism in the study area is caused by multiple factors, including rapid loading, diapir intrusions, hydrocarbon generation, and the thermal expansion of pore fluids. Due to this multi-factor interaction, a cloud of HTHP hydrocarbon reservoirs has developed in the Ying-Qiong Basin, but only traditional PPTs have been implemented, based on the assumption of conditions that do not conform to the actual geologic environment, e.g., Bellotti's law and Eaton's law. In this paper, we focus on these issues, identify some challenges and solutions, and call for further PPT research to address the drawbacks of previous works and meet the challenges associated with the deepwater technology gap. In this way, we hope to contribute to the improved accuracy of geopressure prediction prior to drilling and provide support for future HTHP drilling offshore Hainan Island.

Prediction of Fracture-Cavity System in Carbonate Reservoir： A Case Study in the Tahe Oilfield

The carbonate rocks in Tahe oilfield, which suffered from multi-period polycycle karstification and structure deformation, are heterogeneous reservoirs that are rich in pores, cavities,and fractures. The reservoirs are diversified in scale, space configuration, and complex in filling. For this kind of reservoir, a suite of seismic prestack or poststack prediction techniques has been developed based on the separation of seismic wave fields. Through cross-verification of the estimated results,a detailed description of palaeogeomorphology and structural features such as pores, cavities, and fractures in unaka has been achieved, the understanding of the spatial distribution of reservoir improved.

Reservoir prediction using multi-wave seismic attributes

The main problems in seismic attribute technology are the redundancy of data and the uncertainty of attributes, and these problems become much more serious in multi-wave seismic exploration. Data redundancy will increase the burden on interpreters, occupy large computer memory, take much more computing time, conceal the effective information, and especially cause the ＂curse of dimension＂. Uncertainty of attributes will reduce the accuracy of rebuilding the relationship between attributes and geological significance. In order to solve these problems, we study methods of principal component analysis （PCA）, independent component analysis （ICA） for attribute optimization and support vector machine （SVM） for reservoir prediction. We propose a flow chart of multi-wave seismic attribute process and further apply it to multi-wave seismic reservoir prediction. The processing results of real seismic data demonstrate that reservoir prediction based on combination of PP- and PS-wave attributes, compared with that based on traditional PP-wave attributes, can improve the prediction accuracy.

Sedimentary Characteristics and Reservoir Prediction of Paleogene in the East Part of Kuqa Foreland Basin

Most of the Mesozoic and Cenozoic large-scale hydrocarbon-bearing basins in western China were formed in a similar foreland setting. Hydrocarbon exploration of the Kuqa foreland basin requires research into the sedimentary characteristics and filling evolution of the depositional sequences and their response to the basin process. Based on an analysis of outcrops, well logs and high resolution seismic data, the sedimentary system types and distribution characteristics of the Paleogene in the east part of Kuqa foreland basin were systematically studied. The results show that： （ 1 ） Three types of sedimentary systems are developed in the area： an oxidative salty wide shallow lacustrine system, a fan delta system and an evaporitic bordersea system. （2） The configuration and evolution of the depositional systems of the Paleogene in the Kuqa foreland basin were predominantly determined by foreland tectonism. Vertically, the Paleogene sedimentary sequence can be divided into three parts： the lower, middle and upper depositional system tracts. The lower and upper tracts commonly consist of progradational or aggradational sequences, while the middle part is usually comprised of a set of aggradational to transgressive third-order sequences. Laterally, the sedimentary systems in the east part of the Kuqa foreland basin spread from east to west as a whole, and the sedimentary facies obviously vary from south to north. The sand bodies of the delta front facies are excellent gas reservoirs, characterized by rather thick, extensive and continuous distribution, high porosity and permeability, and just a few barrier beds.

High-Temperature Overpressure Basin Reservoir and Pressure Prediction Model

Yinggehai Basin locates in the northern South China Sea. Since the Cainozoic Era, crust has several strong tension: the basin subsides quickly, the deposition is thick, and the crust is thin. In the central basin, formation pressure coefficient is up to 2.1;Yinggehai Basin is a fomous high-temperature overpressure basin.YinggehaiBasin’s in-depth, especially high-temperature overpressure stratum has numerous large-scale exploration goals. As a result of high-temperature overpressure basin’s perplexing geological conditions and geophysical analysis technical limitations, this field of gas exploration can’t be carried out effectively, which affects the process of gas exploration seriously. A pressure prediction model of the high-temperature overpressure basin in different structural positions is summed up by pressure forecast pattern research in recent years, which can be applied to target wells pre-drilling pressure prediction and post drilling pressure analysis of Yinggehai Basin. The model has small erroneous and high rate of accuracy. The Yinggehai Basin A well drilling is successful in 2010, and gas is discovered in high-temperature overpressure stratum, which proved that reservoir can be found in high-temperature overpressure stratum. It is a great theoretical breakthrough of reservoir knowledge.

Application of Pore Evolution and Fracture Development Coupled Models in the Prediction of Reservoir "Sweet Spots" in Tight Sandstones

The Chang-63 reservoir in the Huaqing area has widely developed tight sandstone ＂thick sand layers, but not reservoirs characterized by rich in oil＂, and it is thus necessary to further study its oil and gas enrichment law. This study builds porosity and fracture development and evolution models in different deposition environments, through core observation, casting thin section, SEM, porosity and permeability analysis, burial history analysis, and ＂four-property-relationships＂ analysis.

Mesozoic Reservoir Predictionin the Longdong Loess Plateau

This paper summarizes a set of interpretation technologies for Mesozoic sandstone reservoir prediction in the Longdong loess plateau, such as seismic sequence processing and interpretation based on generalized S transform, the eroded paleo-geomorphology interpretation of the top of the Triassic and a variety of lateral reservoir predictions. The effects of employing these technologies are compared and analyzed, as well. The research results show that seismic sequence processing interpretation technology based on generalized S transform can distinguish 3ms (about the thickness of 6 m)sequence interface. Consequently the technology can ascertain the distribution of a sand body of the formation Ch 8 and expand the exploration area of the Xifeng oil field in the Longdong area.

Forming Condition and Geology Prediction Techniques of Deep Clastic Reservoirs

1 Introduction As new exploration domain for oil and gas,reservoirs with low porosity and low permeability have become a hotspot in recent years(Li Daopin,1997).With the improvement of technology,low porosity and low

Origin, hydrocarbon accumulation and oil-gas enrichment of fault-karst carbonate reservoirs: A case study of Ordovician carbonate reservoirs in South Tahe area of Halahatang oilfield, Tarim Basin

Based on comprehensive analysis of tectonic and fault evolution, core, well logging, seismic, drilling, and production data, the reservoir space characteristic, distribution, origin of fault-karst carbonate reservoir in Yueman block of South Tahe area, Halahatang oilfield, Tarim Basin, were studied systematically. And the regular pattern of hydrocarbon accumulation and enrichment was analyzed systematically based on development practice of the reservoirs. The results show that fault-karst carbonate reservoirs are distributed in the form of "body by body" discontinuously, heterogeneously and irregularly, which are controlled by the development of faults. Three formation models of fault-karst carbonate reservoirs, namely, the models controlled by the main deep-large fault, the secondary fault and the secondary internal fault, are built. The hydrocarbon accumulation and enrichment of fault-karst carbonate reservoirs is controlled by the spatiotemporal matching relation between hydrocarbon generation period and fault activity, and the size and segmentation of fault. The study results can effectively guide the well deployment and help the efficient development of fault-karst carbonate reservoirs of South Tahe area, Halahatang oilfield.

Prediction of maximum magnitude and original time of reservoir induced seismicity

This paper deals with the prediction of potentially maximum magnitude and origin time for reservoir induced seismicity (RIS). The factor and sign of seismology and geology of RIS has been studied, and the information quantity for magnitude of induced seismicity provided by them has been calculated. In terms of information quan-tity the biggest possible magnitude of RIS is determined. The changes of seismic frequency with time are studied using grey model method, and the time of the biggest change rate is taken as original time of the main shock. The feasibility of methods for predicting magnitude and time has been tested for the reservoir induced seismicity in the Xinfengjiang reservoir, China and the Koyna reservoir, India.