Petroleum最新文献

The technique of Enhanced Gas Recovery by CO₂ injection (CO₂-EGR) into shale reservoirs has brought increasing attention in the recent decade. CO₂-EGR is a complex geophysical process that is controlled by several parameters of shale properties and engineering design. Nevertheless, more challenges arise when simulating and predicting CO₂/CH₄ displacement within the complex pore systems of shales. Therefore, the petroleum industry is in need of developing a cost-effective tool/approach to evaluate the potential of applying CO₂ injection to shale reservoirs. In recent years, machine learning applications have gained enormous interest due to their high-speed performance in handling complex data and efficiently solving practical problems. Thus, this work proposes a solution by developing a supervised machine learning (ML) based model to preliminary evaluate CO₂-EGR efficiency. Data used for this work was drawn across a wide range of simulation sensitivity studies and experimental investigations. In this work, linear regression and artificial neural networks (ANNs) implementations were considered for predicting the incremental enhanced CH₄. Based on the model performance in training and validation sets, our accuracy comparison showed that (ANNs) algorithms gave 15% higher accuracy in predicting the enhanced CH₄ compared to the linear regression model. To ensure the model is more generalizable, the size of hidden layers of ANNs was adjusted to improve the generalization ability of ANNs model. Among ANNs models presented, ANNs of 100 hidden layer size gave the best predictive performance with the coefficient of determination (R²) of 0.78 compared to the linear regression model with R² of 0.68. Our developed ML-based model presents a powerful, reliable and cost-effective tool which can accurately predict the incremental enhanced CH₄ by CO₂ injection in shale gas reservoirs.

As an effective method to prolong the life of mature field, conformance control in water-injection well has been used wildly. Naturally, effect evaluation of conformance control has attracted great attention because it is an important guideline for the design of later enhanced oil recovery (EOR) plan. Usually, production responses such as excessive water reduction and oil production increment are widely used as the indicators. However, production responses may be unreliable due to the difficulty in determining an effective injection well which is caused by a large number of treated water-injection wells in a well group. Therefore, with the application of fuzzy comprehension evaluation (FCE), five evaluation indexes (injection pressure, injectivity index, slope of hall curve, variation coefficient and homogenization coefficient of injection profile) describe injection responses were selected to establish a new evaluation method in this paper. Based on fuzzy mathematics, FCE reflects the difference of evaluation units. Meanwhile, weights of evaluation indexes were obtained by analytic hierarchy process (AHP), which made the results more convincing. Taking Bai 239 oilfield as an example, the five injection responses indexes were used to assess treatment effect on five water-injection wells by single index evaluation and FCE. The results showed that among the five evaluation indexes mentioned above, the slope of hall curve was the most important factor affected evaluation result. In single index evaluation, opposite results may be produced easily on account of the one-sidedness of single index or human error. Furthermore, we found that effective treatment was a relative concept actually. The result of FCE was consistent with single index evaluation but FCE was more acceptable. This study suggests that FCE could be applied to another field such as water flooding, acidizing and hydraulic fracturing

Rock samples' TOC content is the best indicator of the organic matter in source rocks. The origin rock samples’ analysis is used to calculate it manually by specialists. This method requires time and resources because it relies on samples from many well intervals in source rocks. Therefore, research has been done to aid this effort. Machine learning algorithms can estimate total organic carbon instead of well logs and stratigraphic studies. In light of these efforts, the current work present a study on automating the total organic carbon estimation using machine learning approaches improved by an evolutionary methodology to give the model flexibility and precision. Genetic algorithms, differential evolution, particle swarm optimization, grey wolf optimization, artificial bee colony, and evolution strategies were used to improve machine learning models to predict TOC. The six metaheuristics were integrated into four machine learning methods: extreme learning machine, elastic net linear model, linear support vector regression, and multivariate adaptive regression splines. Core samples from the YuDong-Nan shale gas field, located in the Sichuan basin, were used to evaluate the hybrid strategy. The findings show that combining machine learning models with an evolutionary algorithms in a hybrid fashion produce flexible models that accurately predict TOC. The results show that, independent of the metaheuristic used to guide the model selection, optimized extreme learning machines attained the best performance scores according to six metrics. Such hybrid models can be used in exploratory geological research, particularly for unconventional oil and gas resources.

The main objective of this study is to develop the optimal semi-analytical modeling for the infinite-conductivity horizontal well performance under rectangular bounded reservoir based on a new instantaneous source function. The available semi-analytical infinite-conductivity models (ICMs) for horizontal well under rectangular bounded reservoir in literature were developed by applying superposition of pressures in space (SPS). A new instantaneous source function (i.e., instantaneous uniform-flux segmentary source function under bounded reservoir) is derived to be used instead of SPS to develop the optimal semi-analytical ICM. The new semi-analytical ICM is verified with ICM of Schlumberger [1] and with previous semi-analytical ICMs in terms of bottom hole pressure (BHP) profile and inflow rate distribution along the wellbore. The model is also validated with real horizontal wells in terms of inflow rate distribution along the wellbore. The results show that the developed model gives the optimal semi-analytical modeling for the infinite-conductivity horizontal well performance under rectangular bounded reservoir. Besides that, high computational-efficiency and high-resolution of wellbore discretization have been achieved (i.e., wellbore segment number could be tens of hundreds depending on solution requirement). The results also show that at pseudo-steady state (PSS) flow regime, inflow rate distribution along the wellbore by previous semi-analytical ICMs is stabilized U-shaped as performance of inflow rate distribution at late radial flow regime. Therefore, the previous semi-analytical ICMs are incorrectly modeling inflow rate distribution at PSS flow regime due to the negative influence of applying SPS. The optimal semi-analytical ICM is in a general form and real time domain, and can be applicable for 3D horizontal well and 2D vertical fracture well under infinite and rectangular bounded reservoirs, of uniform-flux and infinite-conductivity wellbore conditions at any time of well life.

The novelties in this study are as follows:

1. At PSS flow regime:

(1) Inflow rate distribution along the wellbore is stabilized uniform-flux which was verified mathematically.

(2) Primary pressure derivative (PPD) (i.e., PPD = ∂P_Dt/∂t_DA) is equal to (2π/m_t) for any well and reservoir configurations and depends only on half-length wellbore segments number (m_t).

2. The new ICM gives different trend of Bourdet derivative for the first three flow regimes (i.e., early radial, early linear, late radial) and gives the same trend of Bourdet derivative for PSS flow regime, to their counterparts by uniform-flux model (UFM). The trend of pressure derivatives by UFM for any flow regime is well studied in literature, while the counterparts by ICM are new and need detailed study.

The capacitance-resistance model (CRM) has been a useful physics-based tool for obtaining production forecasts for decades. However, the model's limitations make it difficult to work with real field cases, where a lot of various events happen. Such events often include new well commissioning (NWC). We introduce a workflow that combines CRM concepts and kriging into a single tool to handle these types of events during history matching. Moreover, it can be used for selecting a new well placement during infill drilling. To make the workflow even more versatile, an improved version of CRM was used. It takes into account wells shut-ins and performed workovers by additional adjustment of the model coefficients. By preliminary re-weighing and interpolating these coefficients using kriging, the coefficients for potential wells can be determined. The approach was validated using both synthetic and real datasets, from which the cases of putting new wells into operation were selected. The workflow allows a fast assessment of future well performance with a minimal set of reservoir data. This way, a lot of well placement scenarios can be considered, and the best ones could be chosen for more detailed studies.

Water injection for oil displacement is one of the most effective ways to develop fractured-vuggy carbonate reservoirs. With the increase in the number of rounds of water injection, the development effect gradually fails. The emergence of high-pressure capacity expansion and water injection technology allows increased production from old wells. Although high-pressure capacity expansion and water injection technology has been implemented in practice for nearly 10 years in fractured-vuggy reservoirs, its mechanism remains unclear, and the water injection curve is not apparent. In the past, evaluating its effect could only be done by measuring the injection-production volume. In this study, we analyze the mechanism of high-pressure capacity expansion and water injection. We propose a fluid exchange index for high-pressure capacity expansion and water injection and establish a discrete model suitable for high-pressure capacity expansion and water injection curves in fractured-vuggy reservoirs. We propose the following mechanisms: replenishing energy, increasing energy, replacing energy, and releasing energy. The above mechanisms can be identified by the high-pressure capacity expansion and water injection curve of the well HA6X in the Halahatang Oilfield in the Tarim Basin. By solving the basic model, the relative errors of Reservoirs I and II are found to be 1.9% and 1.5%, respectively, and the application of field examples demonstrates that our proposed high-pressure capacity expansion and water injection indicator curve is reasonable and reliable. This research can provide theoretical support for high-pressure capacity expansion and water injection technology in fracture-vuggy carbonate reservoirs.

Sand production along with the oil/gas detrimentally affects the oil production rate, downhole & subsurface facilities. Mechanical equipment and various chemicals like epoxy resin, furan resin, phenolic resin, etc. are used in the industry to reduce or eliminate this problem. In the present study, a blend of organic and inorganic silicates are used to consolidate loose sand in the presence and absence of crude oil using a core flooding apparatus. The effects of chemical concentration, pH, curing temperature and time, and the presence of residual oil on the consolidation treatment results such as compressive strength and permeability retention, were investigated and optimized. FT-IR and FE-SEM characterization techniques were employed to investigate the interaction between the chemical molecules and the sand grains. The current binding agent exhibited a viscosity of less than 6 cP at room temperature, which facilitates efficient pumping of binding agent into the desired formation through the well bore. The developed mixture demonstrated consolidation properties across all pH conditions. Furthermore, during the experimental investigation, the curing time and temperature was carefully optimized at 12 h and 423.15K, respectively to achieve the highest compressive strength of 2021 psi while achieving the permeability retention of 64%. The current chemical system exhibited improved consolidation capacity and can be effectively utilized for sand consolidation treatment in high-temperature formations.

The sedimentary environment of the Upper Triassic in the southeastern Sichuan Basin is obviously controlled by Luzhou paleo-uplift (LPU). However, the influence of paleo-uplift on the sedimentary patterns of the initial stages of this period in the southeastern Sichuan Basin has not yet been clear, which has plagued oil and gas exploration and development. This study shows that there is a marine sedimentary sequence, which is considered to be the first member of Xujiahe Formation (T₃X¹) in the southeastern Sichuan Basin. The development of LPU resulted in the sedimentary differences between the eastern and western Sichuan Basin recording T₃X¹ and controlled the regional sedimentary pattern. The western part is dominated by marine sediments, but the eastern paleo-uplift area is dominated by continental sedimentation in the early stage of T₃X¹, and it begins to transform into a marine sedimentary environment consistent with the whole basin in the late stage of the period recorded by the Xujiahe Formation. The evidences are as follows: (1) time series: based on the cyclostratigraphy analysis of Xindianzi section and Well D2, in the southeastern Sichuan Basin, the period of sedimentation of the Xujiahe Formation is about 5.9 Ma, which is basically consistent with the Qilixia section, eastern Sichuan basin, where the Xujiahe Formation is widely considered to be relatively complete; (2) distribution and evolution of palaeobiology: based on analysis of abundance evolution of major spore-pollen, many land plant fossils are preserved in the lower part of T₃X¹, indicates the sedimentary environment of continental facies. In the upper part of T₃X¹, the fossil of terrestrial plants decreased, while the fossil of marine and tidal environment appeared, this means that it was affected by the sea water in the late stages of T₃X¹; (3) geochemistry: calculate the salinity of water from element indicates that the uplift area is continental sedimentary environment in the early stage of T₃X¹, while the central and western areas of the basin are marine sedimentary environment. Until the late stage of T₃X¹, the southeast of the basin gradually turns into marine sedimentary environment, consisting with the whole basin; (4) types of kerogen: type Ⅲ kerogen representing continental facies was developed in the early stage of T₃X¹ in the uplift area, and type Ⅱ kerogen, representing marine facies, was developed in the late stage; while type Ⅱ kerogen was developed in the central and western regions of the basin as a whole in T₃X¹. This study is of great significance for understanding of both stratigraphic division and sedimentary evolution providing theoretical support for the exploration and development of oil and gas.

The majority of published empirical correlations and mechanistic models are unable to provide accurate flowing bottom-hole pressure (FBHP) predictions when real-time field well data are used. This is because the empirical correlations and the empirical closure correlations for the mechanistic models were developed with experimental datasets. In addition, most machine learning (ML) FBHP prediction models were constructed with real-time well data points and published without any visible mathematical equation. This makes it difficult for other readers to use these ML models since the datasets used in their development are not open-source. This study presents a white-box adaptive neuro-fuzzy inference system (ANFIS) model for real-time prediction of multiphase FBHP in wellbores. 1001 real well data points and 1001 normalized well data points were used in constructing twenty-eight different Takagi–Sugeno fuzzy inference systems (FIS) structures. The dataset was divided into two sets; 80% for training and 20% for testing. Statistical performance analysis showed that a FIS with a 0.3 range of influence and trained with a normalized dataset achieved the best FBHP prediction performance. The optimal ANFIS black-box model was then translated into the ANFIS white-box model with the Gaussian input and the linear output membership functions and the extracted tuned premise and consequence parameter sets. Trend analysis revealed that the novel ANFIS model correctly simulates the anticipated effect of input parameters on FBHP. In addition, graphical and statistical error analyses revealed that the novel ANFIS model performed better than published mechanistic models, empirical correlations, and machine learning models. New training datasets covering wider input parameter ranges should be added to the original training dataset to improve the model's range of applicability and accuracy.

Hydraulic fracturing (HF) technology can safely and efficiently increase the permeability of coal seam, which is conducive to CBM exploration and prevent coal and gas outburst. However, conventional HF fractures tend to expand in the direction of maximum principal stress, which may be inconsistent with the direction of fracturing required by the project. Therefore, the increased direction of coal seam permeability is different from that expected. To solve these problems, PFC2D software simulation is used to study directional hydraulic fracturing (DHF), that is the combination of slotting and hydraulic fracturing. The effects of different slotting angles ($\theta$), different horizontal stress difference coefficients ($K$) and different injection pressures on DHF fracture propagation are analyzed. The results show that the DHF method can overcome the dominant effect of initial in-situ stress on the propagation direction of hydraulic fractures and control the propagation of fractures along and perpendicular to the slotting direction when $\theta$, $K$ and liquid injection pressure are small. When the DHF fracture is connected with manual slotting, the pressure will shake violently, and the fracturing curve presents a multi-peak type. The increase and decrease of particle pressure around the fracturing hole reflect the process of pressure accumulation and fracture propagation at the fracture tip respectively. Compared with conventional HF, DHF can not only shorten the fracturing time but also make the fracture network more complex, which is more conducive to gas flow. Under the action of in-situ stress, the stress between slots will increase to exceed the maximum horizontal principal stress. Moreover, with the change in fracturing time, the local stress of the model will also change. Hydraulic fractures are always expanding to the area with large local stress. The research results could provide certain help for DHF theoretical research and engineering application.