A new mathematical model for flow towards the well during water production from geothermal reservoirs accounting for fines mobilization, migration and clogging of production well is developed. Treatment of the well discharge data from geothermal reservoir A (Australia) exhibits good agreement with the results of mathematical modeling, which validates the model. The sensitivity analysis based on published laboratory data and theoretical model prediction shows that geothermal reservoirs are particularly vulnerable to fines migration due to reduced electrostatic particle-grain attraction at high temperatures. The modeling shows that large flow rates result in the increased formation damage and productivity impairment.
Accurate and reliable flow rate estimation along the wellbore plays an important role in reservoir development and production management. Traditional methods are expensive, particularly in high temperature wells; and they usually involve running production logsging tools (PLT) together with flowmeters. Deployment of PLTs into a well can be operationally difficult and typically requires flow to be suspended temporarily to allow the tools to be run in hole. This can have a negative impact on the quality of data collected, potentially making it un-representative of normal producing conditions. Alternatively, a simple direct averaging method of the gas flow rate estimation from temperature and pressure data is proposed in this paper, compared to the iterative approach. Solution of the inverse problem allows predicting the flow rate and formation thermal conductivity by matching the gas pressure and temperature profiles with field data. The results of data treatment show good agreement with the model prediction and are consistent with flowmeter (PLT) data, which validates the method developed in this work.
A new stochastic model for suspension/colloidal transport in porous media is developed accounting for the retention mechanism of size exclusion. The medium is modelled by two-pore-size network with triangular pore shape. The micro scale
model is established using distribution functions and the macro scale equation system is generalised. The analytical solution is obtained for deep bed filtration of distributed particles in bimodal media. Comparison between model prediction and experimental data shows good agreement, which validates the proposed model.
A mathematical model is presented for nanoparticle transport and retention in oilfield rocks under net repulsion condition. The model accounts for the particle capture rate proportional to the accessible suspension flux, the inlet concentration and outlet
dilution effects. A new formulation for experimental study in engineered porous media allows excluding other mechanisms of particle retention except straining. The laboratory coreflooding tests show good agreement with the modeling results. The
developed model allows calculating different particle concentrations in suspension and in deposit. It allows estimating depth penetration of nanoparticles and their propagation/accumulation in each reservoir point.
The gas flow rate and the formation thermal conductivity distributions along the gas well have been determined from the measured pressure and temperature profiles using production logging tools, which is much cheaper and more precise than the traditional procedures of direct flow metering. An effective and robust method is proposed in this work: the system of governing equations for non-isothermal gas flow in vertical well is solved using the Runge-Kutta method; then the flow rate and formation thermal conductivity are obtained by optimising the modelled profiles of pressure and temperature based on the measured profiles. Application of the algorithm to field case shows good agreement between the directly measured and modelled pressure and temperature profiles; the flow rate prediction is consistent with flowmeter (PLT) data; the thermal conductivity profile is also in a good agreement with that obtained from lithology log. It validates the proposed method.
Theoretical and laboratory studies are performed aiming at the development of a predictive model for transport and retention of particles from produced water and crude oil/gas based on grain size distribution and particle sizes only. The particle capture by straining is thoroughly investigated. In the laboratory, injections of different sized particles suspended in fluid with different salinity and pH values have been carried out into the newly designed porous media holder with single-layer grains
and into the column packed engineering porous media. The retained particles are filmed using microscope; their breakthrough concentrations are measured by particle counter. A new mathematical model accounting for pore connectivity and triangular pore throat shape is derived and applied to the experimental conditions. Agreement between test data and modelling results supports application of the model to the evaluation of straining effect on the produced water management and injectivity enhancement.
Gas flow profile along the wellbore is an important piece of information for reservoir management purposes, since it provides flow contribution from each producing interval along the wellbore, which is critical for optimizing well performance and
maximizing recovery of reserves. Traditional methods are expensive, particularly in high temperature wells and involve running production logsging tools (PLT's) along with flowmeters. Deployment of production logging tools into a well can be
operationally difficult and typically requires flow to be suspended temporarily to allow the tools to be run in hole. This can have a negative impact on the quality of data collected, potentially making it un-representative of normal producing conditions. A simple and effective method of the gas rate prediction from temperature and pressure data is discussed in this paper. Solving the inverse problem allows determination of the flow rate and thermal conductivity of the formation by matching the gas pressure and temperature distributions with measured profiles. The results of field data treatment show good agreement with the model prediction and are consistent with flowmeter (PLT) data. Besides, the approximate estimate from direct averaging using the measured data shows good accuracy of rate and thermal conductivity prediction.
Aji, Kaiser (University of Adelaide) | McLindin, Cale James (U. of Adelaide) | Saha, Atanu (U. of Adelaide) | Le, Khoi Pham Dang (University of Adelaide) | You, Zhenjiang (University of Adelaide) | Badalyan, Alexander (U. of Adelaide) | Bedrikovetsky, Pavel G.
A new analytical model for deep bed filtration of colloidal/suspension transport under size exclusion particle capture is developed and validated. The laboratory tests on suspension flow in engineered porous media, which simulate the injection of colloidal and particle suspensions in rocks, have been carried out. The model was successfully matched with the laboratory coreflood results, predicting particle breakthrough and stabilised concentration. Compared to the previous population balance model, the present one provides a better prediction of the normalised particle concentration versus jamming ratio. The sensitivity analysis shows that among the three parameters: the mean pore radius, the standard deviation and the inter-chamber distance, the first one has the largest effect on the outlet concentration.
The gas flow profile along the wellbore that includes contribution of all productive layers is an important piece of information for reservoir characterization and well management. It provides flow contribution from each producing interval along the wellbore, which is critical for optimizing well performance and maximizing gas recovery. The present work discussed determination of the rate profile from the pressure and temperature profiles in gas wells, which is significantly cheaper and
more precise than the direct flow metering. An effective and robust algorithm for calculation of the rate and thermal conductivity profiles from the depth pressure and temperature distributions have been developed by tuning the mathematical model of non-isothermal gas flow in vertical well. The detailed sensitivity analysis shows that the inverse problem is wellposed.
The initial values of rate and thermal conductivity for iterative minimization algorithm were obtained by averaging the values directly calculated from the measured pressure and temperature profiles. The initial values are shown to provide the final solution with good accuracy. It allows recommending the initial values for the estimates of the rate profile. Application of the algorithm to field cases shows good agreement between the directly measured and calculated rate profiles; the results are also consistent with flowmeter (PLT) data. It validates the proposed method.