Well Testing At Los Azufres Geothermal Field

1979 ◽  
Author(s):  
Jesus Rivera-R. ◽  
Mehmet Saltuklaroglu
Keyword(s):  
Geothermal Field ◽  
Well Testing

scholarly journals An Approach for Estimating Geothermal Reservoir Productivity under Access Limitations Associated with Snowy and Mountainous Prospects

2021 ◽  
Author(s):  
Mitsuo Matsumoto
Keyword(s):  
Early Time ◽  
Geothermal Field ◽  
Geothermal Reservoir ◽  
Well Testing ◽  
Reservoir Pressure ◽  
New Approach ◽  
Frequency Distributions ◽  
Arbitrary Time ◽  
Active Exploration ◽  
Time Limitations

This chapter describes an approach to estimate reservoir productivity during the active exploration and development of a geothermal prospect. This approach allows a reservoir model to be updated by overcoming the severe time limitations associated with accessing sites for drilling and well testing under snowy and mountainous conditions. Performed in parallel with the conventional standard approach, the new approach enables us to obtain a first estimate of the reservoir productivity at an early time and to make successful project management decisions. Assuming a practical geothermal field, the procedures of the new approach are demonstrated here in detail. Finally, frequency distributions for the expected production rates and changes in the reservoir pressure at an arbitrary time are obtained during an assumed operational period.

Hydrogeological modelling of Olkaria domes geothermal field to predict ground subsidence

2021 ◽  
pp. 25-38
Author(s):  
Solomon Kahiga ◽  
Nicholas Mariita ◽  
Njenga Mburu
Keyword(s):  
Cross Sections ◽  
Surface Deformation ◽  
Three Dimensional ◽  
Computer Software ◽  
Geothermal Field ◽  
Ground Subsidence ◽  
Well Testing ◽  
Resource Exploitation ◽  
Hydrogeological Model ◽  
Software Production

Ground subsidence studies have been done on Olkaria geothermal field conventionally by comparing levels on benchmarks over years. Interferometric synthetic aperture radar (InSAR) systems have also been used to map surface deformation of small spatial extent. For the prediction of future dynamics of land subsidence in Olkaria due to geothermal resource exploitation, a hydrogeological conceptual model has been developed. In this model, hydrologic geothermal fluid properties are analysed and a relationship between the reservoir and geology of the wells established, subsidence is computed numerically. The model takes into account the hydrogeological condition of Olkaria geothermal field. Hydrological reservoir parameters are computed from well testing data. The study considers the Injectivity indices of the various wells under study as pre-computational indicator of the expected subsidence extents. Both two- and three-dimensional geological cross-sections are modelled with the rockworks software by inputting stratigraphic data for Olkaria domes. Geological simulations are used to study subsidence by assigning the ground formation with virtual material that deformed according to some essential relations in Rockworks computer software. Production zones are determined by a comparison between the well properties and corresponding well geology. Subsidence is then computed by the Tezarghi’s modified equation. Cumulative subsidence figures from the computation are in the range of 0.095-0.537m, without any reinjection. Computed values are then mapped in ArcGIS to develop a representative subsidence map. By application of these modelling and numerical computation methods, ground subsidence was effectively predicted using the five selected wells in Olkaria domes field. The hydrogeological model developed, and mapping is an important tool in the planning and development of a reinjection schedule and in subsidence mitigation. Subsidence prediction also is important in design of infrastructure which will be strong enough to resist the forces caused by subsidence.

scholarly journals Wellbore Storage Effects in Geothermal Wells

1980 ◽  
Vol 20 (06) ◽  
pp. 555-566 ◽  
Author(s):  
Constance W. Miller
Keyword(s):  
Flow Rate ◽  
Oil And Gas ◽  
Early Time ◽  
Geothermal Field ◽  
Effect Of Temperature ◽  
Well Testing ◽  
Well Test ◽  
Gas Field ◽  
Temperature Changes ◽  
Wellbore Storage

Abstract The early-time response in the well testing of a homogeneous reservoir customarily is expected to give a unit slope when the logarithm of pressure is plotted vs. the logarithm of time. It is shown that this response is a special case and that another nondimensional parameter must be defined to describe the set of curves that could take place for each value of the wellbore storage coefficient C . In addition, the effect of temperature changes along the bore is shown to increase the time when wellbore storage is important. Introduction The petroleum industry's technique of assessing oil and gas reservoirs by well testing has been extended to the geothermal field by a number of workers. However, at least two important differences between a geothermal field and an oil or gas field must be considered in analyzing geothermal well test data. First the kh/mu value of a geothermal field is usually much larger than that of an oil or gas field because the reservoir thickness h is greater in a geothermal field and the viscosity mu is smaller (k is the permeability). Second, heat loss in the wellbore, which can be ignored in oil and gas fields, is significant in geothermal bores.The concept of wellbore storage - which has been considered quite extensively and refined in such detailed studies as those of Agarwal et al., Wattenberger and Ramey, and Ramey - usually is treated as a boundary condition on the reservoir flow. The boundary condition used is (1) where dp w/dt is the flowing pressure change with time in the wellbore. However, dp w/dt is not necessarily independent of position in the well. When dp w/dt is dependent on the measurement point, a plot of log (p sf) vs. log (t) will not result in a unit slope at early times. This study will consider wellbore storage by looking at the flow in the well itself while treating the reservoir as simple homogeneous radial flow into the well.Heat loss from the well and temperature changes along the bore also have been ignored because oil and gas news can be treated as isothermal. Heat transfer from the well and heating of the fluid in the well is usually a very slow process. When very long times are considered, these temperature effects can become important. Once the early transient behavior is over and a semilog straight line of p sf vs. log(t) is expected in the pseudosteady region, temperature changes in the well can alter the slope of that line so that the slope would no longer be q mu/4 pi kh. The duration and importance of any temperature changes will be considered.A numerical model of transient two-phase flow in the wellbore with heat and mass transfer has been developed. It is used to investigate (1) the early-time interaction of the well flow with that of the reservoir and (2) the longer-time effect of temperature changes on the well test data. Concept of Wellbore Storage Wellbore storage is the capacity of the well to absorb or supply any part of a mass flow rate change out of a well/reservoir system. For a change in flow rate at the surface of the well, the sandface mass flow rate usually is expressed as (2) SPEJ P. 555＾

scholarly journals Application of Data-Driven Discovery Machine Learning Algorithms in Predicting Geothermal Reservoir Temperature from Geophysical Resistivity Method

2021 ◽  
Vol 9 (4) ◽  
pp. 1-12
Author(s):  
Solomon Namaswa ◽  
John Githiri ◽  
Nicholas Mariita ◽  
Maurice K’Orowe ◽  
Nicholas Njiru
Keyword(s):  
Machine Learning ◽  
Geophysical Methods ◽  
Estimation Method ◽  
Geothermal Field ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Support Vector ◽  
Well Testing ◽  
Subsurface Temperature ◽  
Reservoir Temperature

Geophysical methods including seismology, resistivity, gravity, magnetic and electromagnetic have been put in use for geothermal resource mapping at the Great Olkaria Geothermal field for decades. Reservoir temperature distribution and the electrical conductivity of rocks mainly depend on the same parameters such permeability, porosity, fluid salinity and temperature. This research focused on the integration of Olkaria Domes geothermal well testing temperature and geophysical Electromagnetic resistivity data with the aim of establishing an alternative estimation method for temperature of the reservoir through machine learning Analytics. To achieve this, Data-Driven Discovery Predictive Model Algorithm was built using Python programming language on Anaconda framework. The open-source web based application Jupyter Notebook for coding and visualization was used. Decision Tree Regression, Adaptive Booster Regression, Support Vector Regression and Random Forest Regression were used. The model performance was evaluated using R-Score and Mean Absolute Error metrics. Based on these performance score, the best performing model was suggested to predict subsurface temperature from resistivity. Training the model using the DTR algorithm approach provides superior outputs with R2 of 0.81 and lowest MAE of 29.8. The DTR algorithm could be implemented in determination of subsurface Temperature from resistivity in high temperature hydrothermal fields.

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