Capillary pressure and relative permeability are important parameters in reservoir engineering. Techniques frequently used to measure capillary pressure and relative permeability simultaneously are complicated and time-consuming. On the other hand, it is also difficult to measure the two parameters in low permeability rocks. It would be useful if capillary pressure and relative permeability could be inferred from spontaneous imbibition tests. This is because such tests are relatively simple, fast, and economical. In the present study, an analytical approach was developed to determine capillary pressure and relative permeability simultaneously by using experimental data of spontaneous water imbibition into gas-saturated rock. Spontaneous water imbibition tests in different rocks were conducted to test the proposed approach. Both capillary pressure and relative permeability were calculated using the proposed approach and the results were closely equal to those measured using different methods.
This article introduces a multiscale pore structure characterization method using a combination of mercury porosimetry and image analysis. The method was used to determine the distribution of pore volume by pore size and to estimate the pore-to-throat size aspect ratio. The key idea of the method is that pore size distribution obeys a fractal scaling law over a range of pore size. On this basis, scattering intensity data computed from the measured two-point correlation function and those measured from mercury porosimetry are extrapolated in the size range 0.01 µm < r < 1000 µm, using the known fractal scaling law.
A set of siltstone samples taken from Daqing Oilfield was analyzed through this method. Distribution of pore volume by pore size over the entire range of pore length scales was determined. The results demonstrated significant similarities in the pore structure of all samples. The image analysis results were in qualitative agreement with the results of mercury intrusion/extrusion tests.
The results were also compared with some other samples (including siltstone, sandstone, and dolomite) that had been analyzed using similar methods. It is shown that the surface fractal dimension obtained by analysis of MIP data is consistent with the value obtained by image analysis for different samples with different porosity and permeability.
Novel information on the pore-to-throat aspect ratio is obtained by comparing the complete pore volume distribution (PVD) to the MIP data.
The main challenge facing the oil industry is to reduce development costs while accelerating recovery while maximising reserves. One of the key enabling technologies in this area is intelligent well completions. Downhole inflow control devices allow for the flexible operation of non-conventional wells. By placing sensors and control valves at the reservoir face, engineers can monitor reservoir and well performance in real time, analyse data, make decisions and modify the completion without physical intervention to optimise reservoir and asset performance. They provide the ability to independently control each valve individually from the surface to maximise oil production and/or minimise formation water and/or gas production. However, they may also be used to address other produced water management issues, such as inorganic scale control.
This paper describes the potential risks posed specifically to intelligent completions by scale deposition. The potential benefits to scale management that ICVs, such as control of scaling brine production and effective scale inhibitor placement, are described. Calculations are performed for a North Sea field with a barite scaling risk, and the cost benefit - specifically to scale management - of using ICVs is evaluated. These calculations demonstrate that intelligent completions significantly reduce the scale inhibitor chemical costs while improved scale inhibitor placement is achieved.
Inflow control valves (ICVs) are emerging as a very promising technology for minimising water production and optimising hydrocarbon recovery.[1-6] However, they also have the potential to address other produced water management issues, such as inorganic scale control, while the operation of the ICV and may in turn be affected by these produced water management issues. This paper aims to demonstrate the possible benefits that can be added to scale management as result of using ICV technology. It also investigates the potential risks posed to intelligent completions by the deposition of scale. The technical feasibility and economic viability of implementing intelligent well technology for scale management was studied. A case study illustrates how the ICVs may be used for scale management. Such a study that links the use of the intelligent wells and scale management has not been reported previously.
Figs. 1 and 2 illustrate some of the issues that may affect the performance of production tubing, and how better access to downhole information can improve the ability to deliver flow assurance.
Qi, Zhilin (Zhongyuan Oil Field) | Du, Zhimin (Southwest Petroleum Inst.) | Liang, Baosheng (Univeristy of Texas at Austin) | Tang, Yong (Southwest Petroleum University) | Wang, Shouping (Research Institute of Petroleum Exploration and Development, Zhongyuan Oil Field) | Deng, Ruijian (Research Institute of Petroleum Exploration and Development, Zhongyuan Oil Field)
As a type of complex well, snaky well is pretty promising especially in the geometrically complicated reservoirs and has been reported recently. Accurate prediction of well productivity is vital. However, conventional productivity calculation methods for horizontal wells could not be applied directly for snaky wells because the production part in the well bore has large fluctuation in the vertical direction and therefore the wellbore pressure drop should not be neglected.
On the basis of the distribution of velocity potential produced in an infinite reservoir by one snaky well, this paper uses the mirror image and the principle of superposition and obtains the velocity potential and pressure distributions for bottom water drive reservoirs with and without gas cap, and edge water drive reservoir. Along the well bore, we then study the flow characteristics and establish a pressure drop model. Coupling wellbore flow with fluid flow in the formation, we develop and further solve a productivity model for the snaky well.
The high precision and practicability are clearly demonstrated using an example. Such well productivity approach reduces uncertainty and better satisfies the real condition, resulting in more accurate prediction.
Most of the oil production in the Middle East comes from carbonate reservoirs, the majority of which are fractured. These reservoirs tend to produce at high rates in their early production period followed by low rates later on, leading to low overall recovery. The challenge is to manage the field and arrest the production decline for a long time.
A reservoir simulation study was performed on a fractured Middle Eastern carbonate field to determine the optimal production strategy. Three possible scenarios - natural depletion, gas injection and water injection were compared. Results indicated that water injection yields better recoveries than gas injection and natural depletion; this is expected since the rock is intermediate to oil-wet, meaning that there was high recovery from imbibition in water flooding. The presence of connected fractures led to early breakthrough and low recoveries in gas injection scenarios. The different physical mechanisms affecting oil recovery are discussed and recommendations are made for other fields with the same fracture properties and wettability
We adapted a scratch apparatus used to evaluate shear strength and stiffness of rocks to use on soft filter cakes, to obtain quantitative information on cake properties for modelling purposes. The modelling will help design better drilling fluids, in terms of their filter cake's resistance to oil production onset. Scratch testing of rock specimens is now a well established method to obtain reliable strength and stiffness measurements as a function of distance along the scratched surface. The apparatus consists of a rigid frame holding a cutter, being pushed at a constant velocity over a rock specimen. A micrometric screw allows the user to choose a precise cutting depth, while a bidirectional load cell monitors the shear and normal forces on the cutter. The shear force can be related to the specific energy of cutting, which in turn correlates with the Unconfined Compressive Strength (UCS) of the rock. New low-resolution load cells have been installed in anticipation of the orders of magnitude lower expected values when scraping filter cakes. Rocks were exposed to different fluids to assess the impact on filter cake quality and inner filter cake properties. The effect of the internal filter cake on the rock was also addressed by scratching the rocks prior to filtration and once again after, scratching through the filter cake, on the exposed rock face. Quantitative and detailed probing of filter cake elastic properties is now possible using the scratch method. Concerns about removing the filter cake all at once (especially when OBM systems are tested, known for their low adhesion) turned out to be unfounded; the tool was capable of scraping out clean, well-defined cake layers, down to a thickness of 0.1 mm.
Recently our business has had great success in developing tools for using full physics simulators in Assisted History Matching (AHM) and prediction of future performance. However, the end user is often left wondering what to do with the large number of forecasts from these studies; any optimisation of the remaining potential has to be done manually.
This paper discusses a method in which the principles of AHM are used to optimise the development. In AHM the practitioner attempts to minimise the error between the observed and simulated data; the ‘objective function'. A large number of ‘geological' variables to the simulator are sensitised. ‘Control' variables are used in the predictive work. These are only limited by the scheduling features of the reservoir simulator and would normally be the number and location of development wells; change in facilities constraints; re-completion; workovers etc. The objective function for development optimisation tends to be an economic one, i.e. Net Present Value (NPV) or some other profitability indicator. The method described in this paper has an advanced feature for generating additional results based on the simulator output time series.
Schedule optimisation is known to be a very difficult problem, not amenable to standard mathematical programming algorithms, and we describe how, in conjunction with the use of a proxy model, we have successfully applied genetic algorithm optimization techniques, to solve the scheduling problem with minimal CPU processing.
The method has been used a number of times and its application is described by some case studies; optimising the number and location of development wells and timing of scheduling events. As a very large number of possible combinations of variables are used the optimisation tends to be less subjective than a manual giving generally better results.
Proxy models are becoming more widely used as they can simplify highly complex processes with reasonable accuracy. Especially in risk analysis, where complex relationships between the uncertainty parameters exist, proxy models are used in form of response surfaces to accelerate interpretation and optimization methods. However, the use of proxy models is rarely seen in production optimization.
When the data gathering from wells and surface equipment is fully automated, production optimization can be performed almost in real-time. The bottleneck in this workflow is the high computational effort of simulation models and the large number of input variables to optimize. This disadvantage can be overcome by mimicking the behavior of the system, such as the coupling of a simulation model and the surface network model, by using a computational efficient method. The requirements for such proxy models are high, since they have to capture highly non-linear trends hidden in a small number of representative samples.
This paper presents the usage of neural networks as proxy models. For the production optimization process, genetic algorithms are used. Their advantage lies in the ability to handle a large number of input variables. The neural network operates as fitness function for the genetic algorithm. The optimization result can be achieved extremely fast (within seconds), allowing optimization in near real time. A real life example is also presented in this work.
NAM in the Netherlands is currently conducting studies to redevelop the Schoonebeek oil field, onshore in the Netherlands. Steam flooding is the envisaged process.
Large volumes of produced water from this field are to be re-injected in regional depleted Zechstein fractured carbonate gas fields. Estimates of injection rates and volumes are required for reservoir selection and pumping requirements.
This paper demonstrates a methodology which permits injection rate and volume predictions to be made in a simple spreadsheet model based on historical measured gas production rates and volumes. The paper describes how to convert an analytical gas productivity index solution for dual-porosity systems to a water injectivity index. The conversion was validated using rigorous dual-porosity simulations and sensitised to a broad range of matrix and fracture properties. It was found that injectivity in the fractured Zechstein carbonate is constrained by the effective permeability of the fracture system and is relatively insensitive to matrix permeability and fracture spacing. This behavior was verified by calculation of a dual-porosity pseudo skin factor. Partially fractured models also demonstrate that some matrix pore space which was capable of producing gas, cannot be effectively accessed by injected water volumes.
The converted water injectivity index combined with other nonlinear repressurisation, relative permeability and water viscosity effects were combined with surface pump curve and wellbore head/friction calculations to construct a spreadsheet capable of predicting long term injectivity on an individual well basis. A large number of wells were screened and optimized using this practical tool.
This methodology can be readily applied to other water disposal projects targeting depleted, naturally fractured or matrix only gas fields.
The estimation of hydrocarbon reserves heavily depends on the accuracy of resistivity data and the reliability of their interpretation. To optimize data information extraction from acquired modern array resistivity logs, integrated inversion-based interpretation techniques are required. However, the direct application of an inversion method may provide an incorrect equivalent solution, resulting in erroneous hydrocarbon estimates.
To solve the equivalency problem, we have developed a new method that reconstructs the formation model in steps using inversion of time-lapse Logging-While-Drilling (LWD) and Wireline (WL) measurements. The main steps are:
1. Estimate resistivities of invaded and uncontaminated zones using LWD data focusing or inversion.
2. Re-estimate resistivity of the invaded zone using WL logs, array data focusing or inversion.
3. Estimate depth of invasion using WL data inversion and the results of the steps above.
4. Re-estimate the model parameters using WL data inversion and the results of the steps above.
5. Estimate parameters of a complex invasion radial profile using WL data inversion.
To establish the effectiveness of the method, we tested it on a synthetic example that was constructed using LWD and WL logs acquired in a well from the South China Sea. This field data was also used for our case study.
The conclusion is that the suggested method provides a framework for effective integration of time-lapse logging measurements to increase accuracy of formation evaluation.