Xinjiang Uyghur Autonomous Region

This paper presents a simple yet rigorous model and provides a methodology to analyze production data from wells exhibiting three-phase flow during the boundary-dominated flow regime. Our model is particularly applicable to analyze production data from volatile oil reservoirs, and should replace the less accurate single-phase models commonly used. The methodology will be useful in rate transient analysis and production forecasting for horizontal wells with multiple fractures in shales. Our analytical model for efficiently handling multi-phase flow is an adaptation of existing single-phase models. We introduce new three-phase parameters, notably fluids properties. We also define three-phase material balance pseudotime and three-phase pseudopressure to linearize governing flow equations. This linearization makes our model applicable to wells with variable rates and flowing pressures. We optimized the saturation-pressure path and further suggested an appropriate method to calculate three-phase pseudopressures. We validated the solutions through comparisons with compositional simulation using commercial software; the excellent agreement demonstrated the accuracy and utility of the analytical solution. We concluded that, during the boundary-dominated flow regime, the saturation-pressure relation given by steady-state path and tank-type model for volatile oil reservoirs leads to satisfactory results. We also confirmed that our definitions of three-phase fluid properties are well suited for ultra-low permeability volatile oil reservoirs. The computation time of our model is greatly reduced compared to a numerical approach, and thus the methodology should be attractive to the industry. Our model is efficient and practical to be applied for production data analysis in ultra-low permeability volatile reservoirs with non-negligible water production during the boundary-dominated flow regime. This study extends existing analytical model methodology for volatile oil reservoirs and is relatively easy for reservoir engineers to understand.

Heavy oil reservoirs are generally unconsolidated and easy to produce sand during production¹. In the late stage of Cyclic Steam Stimulation (CSS), high temperature steam and hot water destroy clay minerals, further aggravate sand production problems, resulting in sand jam, sand burial, pump jam and casing damage, and frequent operations seriously affect the effective production of oil wells. PetroChina has been carrying out steam stimulation for more than 40 years, and has formed a series of sand control technologies in the field of heavy oil thermal recovery. This paper introduces several sand control techniques used in Liaohe and Xinjiang Oilfield and their successful cases. Liaohe and Xinjiang Oilfield is rich in heavy oil resources. The oil types include ordinary heavy oil, extra heavy oil and super heavy oil². In Liaohe Oilfield, they have medium and deep heavy oil with a depth of 600-900m and super deep heavy oil with a depth of 1300-1700m. As the main development method, CSS has entered the late stage of production, more than 9 cycles, and encountered various sanding problems during the production. By using mechanical sand control and chemical sand control measures, the sand path was controlled, and an artificial wellbore was formed in the near-wellbore zone to control the fine silt. Through the discussion in this paper, we can provide a variety of solutions for sanding problems encountered in heavy oil steam handling.

The Kuqa foreland thrust belt, as a secondary tectonic unit of the Tarim basin at the front of the Tianshan Mountains, is a foreland basin that formed in the Late Tertiary. The lower Cretaceous Bashijiqike tight sandstone in the basin is an ultralow-permeability and low-porosity reservoir. The Kuqa foreland thrust belt includes Kela, Keshen, Bozi, Zhongqiu, and Alvart blocks. Although these blocks developed under the same sedimentary conditions, the permeability-porosity relationship and wireline log response can be very different among the blocks. Whereas the shallow zone has been had E&P activities for decades, fully understanding the fluid properties, the porosity-permeability relationship, and distribution pattern of gas in the deep to ultradeep zone is of strategic significance and can provide the experience for the exploration of similar gas reservoirs in China and worldwide. The main target zone depth varies from 6000 m to 8000 m, and the formation pressure is near or exceeds 20,000 psi. Compared to a time-consuming and costly drillstem test (DST) operation, the wireline formation test (WFT) is the most efficient and cost-saving method to confirm hydrocarbon presence. However, the success rate of WFT sampling operations in the deep Kuqa formation is less than 50% overall, mostly due to the formation tightness exceeding the capability of the tools. Therefore, development of an optimized WFT suitable to the formation was critical.

More than 30 WFT wells in Kuqa foreland thrust belt were studied to understand the well and formation conditions causing the success or failure of these WFT operations. By doing a statistical analysis of more than 1000 pressure test points, we researched the relationship between mobility and petrophysical logs such as neutron, density, gamma ray, resistivity, P-sonic, etc. Several statistical mathematic methods were applied during this study, including univariate linear regression (ULR), multiple linear regression (MLR), neural network regression analysis (NNA), and decision tree analysis (DTA) methods. A systematic workflow was formed to mine data information, and we delivered a standard chart of the relationship between mobility and the petrophysical logs, an integrated equation based on MLR, and an NNA model that can be applied to WFT feasibility analysis.

These methods can be considered the foundation of artificial intelligence (AI), which can be used in future mobility automatic prediction. This provides a rough estimation of the mobility and sampling success rate and enables WFT optimization to be conducted in advance.

In complex clastic reservoirs, deviation often exists in oil saturation derived from logging interpretation due to the borehole conditions and log quality. Especially in thin-sand reservoirs, oil saturation is generally lower than actual results because of boundary effect. An innovative approach of saturation height function coupled with rocktype is provided to improve the accuracy of saturation prediction in well logs and spatial distribution. The model results are compared with log derived results.

The new approach is based on the routine and special core analysis of over 100 core samples from the complex clastic reservoir in the north of Albert Basin in Uganda. Discrete rocktypes (DRT) are determined by flow zone index and pore throat radius which indicate the fluid flows. After converting the capillary pressure (Pc) data to reservoir conditions, Lambda curve fitting (Sw = A * PcB + C) is used to fit each capillary pressure curve. Then, a robust relationship between the fitting coefficients (A, B, C) and rock properties (i.e. porosity and permeability) is expressed as a nonlinear function for each DRT. Combined with the height above free water level, a water saturation (Sw) model is constructed by SHF within DRT model.

Using the porosity and permeability obtainedfrom routine core analysis, FZI and pore throat radius are calculated (e.g., by Winland function). Five different rocktypes (DRT1-5) are defined in the delta sand reservoir in the north of Albert Basin with distinct pore textures. The distinguishment is in accordance with the shape of capillary pressure curve, that is, the flow capability increases from DRT1 to DRT5. A strong correlation between Pc and Sw processed by Lambda curve is acquired for each core sample. Meanwhile, 3 coefficients A, B and C can be obtained in Lambda formula. By nonlinear regression, coherent relation between each factor and reservoir properties (porosity and permeability) for each DRT are obtained. Height above the free water level is estimated by geometrical modeling on the oil water contact. The Sw model is constructed by the new SHF function coupled with DRT model. It showed that the water saturation derived from SHF is highly consistent with log derived results and NMR results. Moreover, it provides more precise results in thinner sands and in spatial distribution.

Based on the identified different rocktype, a new SHF derived from capillary pressure data is utilized to establish the relationship between saturation, the height above the free water level and rock properties. The approach can significantly improve the accuracy of saturation prediction of thin reservoir and reasonably depict the spatial distribution characteristics of saturation. Furthermore, the approach will provide a more precise result in hydrocarbon volume calculation and numerical simulation.

The seismic inversion method using the seismic onset times has shown great promise for integrating real- continuous seismic surveys for updating geologic models. However, due to the high cost of seismic surveys, such frequent seismic surveys are not commonly available. In this study, we focus on analyzing the impact of seismic survey frequency on the onset time approach, aiming to extend the advantages of onset time approach when infrequent seismic surveys are available.

To analyze the impact of seismic survey frequency on the onset time approach, first, we conduct a sensitivity analysis based on the frequent seismic survey data (over 175 surveys) of steam injection in a heavy oil reservoir (Peace River Unit) in Canada. The calculated onset time maps based on seismic survey data sampled at various time intervals from the frequent data sets are compared to examine the need and effectiveness of interpolation between surveys. Additionally, we compare the onset time inversion with the traditional seismic amplitude inversion and quantitatively investigate the nonlinearity and robustness for these two inversion methods.

The sensitivity analysis shows that using interpolation between seismic surveys to calculate the onset time an adequate onset time map can be extracted from the infrequent seismic surveys. This holds good as long as there are no changes in the underlying physical mechanisms during the interpolation period. A 2D waterflooding case demonstrates the necessity of interpolation for large time span between the seismic surveys and obtaining more accurate model update and efficient data misfit reduction during the inversion. The SPE Brugge benchmark case shows that the onset time inversion method obtains comparable permeability update as the traditional seismic amplitude inversion method while being much more efficient. This results from the significant data reduction achieved by integrating a single onset time map rather than multiple sets of amplitude maps. The onset time approach also achieves superior convergence performance resulting from its quasi-linear properties. It is found that the nonlinearity of the onset time method can be smaller than that of the amplitude inversion method by several orders of magnitude.

CO₂ injection, either miscible or immiscible, has been recognized as a promising method to enhance oil production for tight reservoirs, with major projects in progress worldwide. This work targeted a tight sandstone reservoir in China, located in the Lucaogou formation of Jimsar sag, Junggar Basin. CO₂ injection using huff-puff method was planned to stimulate the oil production because of the rapidly declining productivity of the existing horizontal wells. Although some laboratory works have been conducted for this site, there still lacks the knowledge of mobilizing process of the matrix oil when natural fractures are present. Herein, we present an experimental study of CO₂ huff-n-puff in a fractured sandstone rock with the primary objective of elucidating the oil recovery dynamics in different phases under reservoir conditions. The results indicated that the oil recovery rate rapidly decreased with cycle numbers and CO₂ huff-n-puff primarily recovered the oil in the large matrix pores. After three cycles, an incremential 32.2% of the original oil-in-place (OOIP) was produced. Based on the dynamics of oil mobilization, the main-determining-forces (MDFs) in this process were re-defined. CO₂ displacement, CO₂-oil interaction driven by diffusion, and depressurization dominated the first cycle, whereas from the second cycle the first two forces became insignificant. This implied that the soaking phase could be minimized or even eliminated from the second cycle in order to reduce the shut-in time in field application.

This course discusses the fundamental sand control considerations involved in completing a well and introduces the various sand control techniques commonly used across the industry, including standalone screens, gravel packs, high rate water packs and frac-packs. It requires only a basic understanding of oilfield operations and is intended for drilling, completion and production personnel with some sand control experience who are looking to gain a better understanding of each technique’s advantages, limitations and application window for use in their upcoming completions.

Pore structure is of great importance in tight reservoirs identification and validation evaluation, especially for formations with developed fractured. However, the conventional pore structure evaluation method based on nuclear magnetic resonance (NMR) logging lost its role. This is because the fractures with width lower than 2mm did not have response in the NMR T2 spectrum. Whereas the porosity spectrum, which extracted from the FMI data, was considered to be effective in fractured reservoir pore structure evaluation. In this study, to quantitatively characterize tight glutenite reservoir pore structure in the Jiamuhe Formation in northwest margin of Junggar Basin, northwest China, 90 core samples were drilled for lab mercury injection capillary pressure (MICP) measurement, and the XRMI data (acquired by the Halliburton and be similar with FMI) was processed to acquire the porosity spectrum.

Automated image processing algorithms can improve the quality and speed of classifying the morphology of heterogeneous carbonate rock. Several commercial products have worked to produce petrophysical properties from 2D images and with less extent from 3D images, relying on image processing and flow simulation. Images are mainly micro-computed tomography (μCT), optical images of thin-section, or magnetic resonance images (MRI). However, most of the successful work is from the homogeneous and clastic rocks. In this work, we have demonstrated a Machine Learning assisted Image Recognition (MLIR) approach to determine the porosity and lithology of heterogeneous carbonate rock by analyzing 3D images form μCT and MRI. Our research method consists of two parts: experimental and MLIR. Experimentally, we measured porosity of rock core plug with three different ways: (i) weight difference of dry and saturated rock, (ii) NMR T₂ relaxation of saturated rock, and (iii) helium gas injection of rock after cleaning and drying.

We performed MLIR on 3D μCT and MRI images using random forest machine-learning algorithm. Petrophysicist provided a set of training data with classes (i.e., limestone, pyrite, and pore) as expert knowledge of μCT Image intensity correspondence to petrophysical properties. MLIR performed, alone, each task for identifying different lithology types and porosity. Determined volumes have been checked and confirmed with three different experimental datasets. The measured porosity, from three experiment-based approaches, is very close. Similarly, the MLR measured porosity produced excellent results comparatively with three experimental measurements, with an accuracy of 97.1% on the training set and 94.4% on blind test prediction.

This seminar will teach participants how to identify, evaluate, and quantify risk and uncertainty in everyday oil and gas economic situations. It reviews the development of pragmatic tools, methods, and understandings for professionals that are applicable to companies of all sizes. The seminar also briefly reviews statistics, the relationship between risk and return, and hedging and future markets. Strategic thinking and planning are key elements in an organisation’s journey to maximise value to shareholders, customers, and employees. Through this workshop, attendees will go through the different processes involved in strategic planning including the elements of organisational SWOT, business scenario and options development, elaboration of strategic options and communication to stakeholders.