A Sand-Erosion Model for Volumetric Sand Predictions in a North Sea Reservoir

2001 ◽  
Vol 4 (01) ◽  
pp. 44-50 ◽  
Author(s):  
Euripides Papamichos ◽  
Eva M. Malmanger

Summary Volumetric sand-production data from a North Sea reservoir are interpreted with respect to the applied drawdown. Two sand rates are identified: the initial sand rate related to the increase of drawdown, and the final sand rate related to the magnitude of drawdown. A sand-erosion model is also presented and used for predicting the field data, and the results compare reasonably with the field measurements. Introduction Sand production has become a most effective way to increase well productivity. The industry reports increases in the sand-free rate up to 44% after sand production. At the same time, downhole sand control is the most common formation damage in the North Sea sandstone reservoirs. Much attention has thus been focused on how to operate wells that produce sand from time to time and how to produce loose sand under controlled conditions. This paper addresses these problems through analysis of field data on volumetric sand production and predictions with a sand-erosion model. The capabilities of the model are demonstrated by estimating the character of sand production in terms of the produced sand in a North Sea reservoir as a function of time and drawdown. Currently a volumetric sand model1 has been developed for heavy-oil reservoirs and predictions of sand amount as a function of the changes in drawdown over time. Field data and numerical simulations on volumetric sand production in a North Sea reservoir are presented. Previous work has mainly concentrated on the prediction of sand-production initiation. The current analysis of the field data and model simulations attempt to establish the relation between volumetric sand rate as a function of time, stresses, and fluid-flow rate. Based on such analyses and model predictions, a well-production strategy can be implemented for maximum productivity with minimum sanding problems. Field-Data Interpretation Volumetric sand-production data were collected from an oil-production well in a North Sea reservoir. Table 1 provides the perforation intervals of the well and other perforation data, such as total perforated length of the well and perforation density and phasing. The well inclination at the perforated interval is 50 with respect to the vertical. Various reservoir data, such as porosity and permeability, in-situ stresses, initial reservoir pressure, and current depletion are given in Table 2. The mechanical properties of the reservoir have been characterized through triaxial compression tests at 2, 5, and 15 MPa confining stress. The triaxial test results from two reservoir intervals are given in Table 3. Volumetric sand-production data from this well have been continuously collected. Fig. 1 shows the sand rate, cumulative sand, and the applied drawdown over a 120-hour production period. In this period, the sand-production rate shows three peaks associated with drawdown increases. After each peak and under near-constant drawdown, the sand rate decreases gradually to a near-constant residual value. The residual constant sand-rate value appears to increase with increasing drawdown. A total of approximately 117 kg of sand was produced. For the same 120-hour period, Fig. 2 shows the productivity of the well expressed as the total fluid rate over drawdown, and the oil fraction of the fluid-flow rate. Both the productivity and the oil fraction are constant during this period at approximately 39 std m3/Mpa·h and 0.68, respectively. For the field-data interpretation, the total period is divided into three time intervals associated with a peak and a subsequent decrease of the sand rate. For these time intervals, Table 4 lists the time duration, the increase in drawdown resulting in a peak in sand rate, and the initial and final drawdowns. The sand rates and drawdowns for the three time intervals are plotted in Fig. 3. The sand rate qsand in each time interval is approximated with the following parabolic function of the time t, which is also plotted in Fig. 3.Equation 1 where qisand is the initial sand production and a and b are calibration constants. The final residual sand rate qfsand may then be expressed asEquation 2 The initial and final sand rates and parameters a and b are listed in Table 5 for the three time intervals. The initial drawdown correlates with the drawdown increase and the final sand rate with the final drawdown, such that they increase with larger drawdown increase or final drawdown, respectively, as shown in Fig. 4. This means that if the drawdown is increased, a peak in the sand rate should be expected. The magnitude of the peak is larger for a larger drawdown increase. After the peak, the sand rate decreases and appears to approach a constant value, which depends now on the magnitude of the drawdown itself and not the increase in drawdown. Integration of Eq. 1 gives the cumulative sand production msand as a function of time; i.e.,Equation 3 The field data for the cumulative sand production in the three intervals are plotted in Fig. 5.

2018 ◽  
Vol 166 ◽  
pp. 208-224 ◽  
Author(s):  
Xiaorong Li ◽  
Yongcun Feng ◽  
K.E. Gray

2021 ◽  
pp. 1-23
Author(s):  
Daniel O'Reilly ◽  
Manouchehr Haghighi ◽  
Mohammad Sayyafzadeh ◽  
Matthew Flett

Summary An approach to the analysis of production data from waterflooded oil fields is proposed in this paper. The method builds on the established techniques of rate-transient analysis (RTA) and extends the analysis period to include the transient- and steady-state effects caused by a water-injection well. This includes the initial rate transient during primary production, the depletion period of boundary-dominated flow (BDF), a transient period after injection starts and diffuses across the reservoir, and the steady-state production that follows. RTA will be applied to immiscible displacement using a graph that can be used to ascertain reservoir properties and evaluate performance aspects of the waterflood. The developed solutions can also be used for accurate and rapid forecasting of all production transience and boundary-dominated behavior at all stages of field life. Rigorous solutions are derived for the transient unit mobility displacement of a reservoir fluid, and for both constant-rate-injection and constant-pressure-injection after a period of reservoir depletion. A simple treatment of two-phase flow is given to extend this to the water/oil-displacement problem. The solutions are analytical and are validated using reservoir simulation and applied to field cases. Individual wells or total fields can be studied with this technique; several examples of both will be given. Practical cases are given for use of the new theory. The equations can be applied to production-data interpretation, production forecasting, injection-water allocation, and for the diagnosis of waterflood-performanceproblems. Correction Note: The y-axis of Fig. 8d was corrected to "Dimensionless Decline Rate Integral, qDdi". No other content was changed.


2000 ◽  
Vol 3 (02) ◽  
pp. 160-164 ◽  
Author(s):  
M.G. Kelkar

Summary Isochronal testing is commonly used to evaluate the performance of gas wells. This paper proposes a new technique to estimate the value of the turbulence coefficient based on isochronal tests. The proposed method is easy to apply and evaluate. Further, the method also provides a value of bg under stabilized conditions which can be used to predict the performance of gas wells under stabilized conditions. The proposed method is validated using field data under a variety of operating conditions. The values of the turbulence coefficient based on the field data can differ significantly compared to the literature correlations. This further shows the importance of obtaining appropriate reservoir parameters based on the field rather than the laboratory data. Introduction The use of isochronal or modified isochronal testing is well established in the gas industry. These tests are common for gas wells which take a long time to reach a stabilized rate. A common example would be a low permeability, fractured reservoir. Instead of testing these wells until a stabilized rate is reached, the wells are tested for a fixed period of time and the bottomhole pressure is measured. For isochronal testing, the well is then shut in until it reaches a stabilized pressure and the procedure is repeated for a different rate. For modified isochronal testing, the well is shut in for a fixed period of time, and the shut-in pressure is measured at the end of that period. The procedure is then repeated at other rates. By repeating this procedure for different time intervals, we can gather information about rate vs. pressure drop in the formation for these time intervals. Ultimately, using this information, our goal is to establish an appropriate rate vs. pressure drop relationship under stabilized conditions. Two procedures are commonly used to establish the equation for rate vs. pressure drop. An empirical method states that q g = C ( p  ̄ 2 − p w f 2 ) n . ( 1 ) We can write a simpler equation in terms of pseudo-real pressures as q g = C [ m ( p  ̄ ) − m ( p w f ) ] n . ( 2 ) Under transient conditions, the value of C is not constant. Instead, we can write Eq. 2 as q g = C ( t ) [ m ( p  ̄ ) − m ( p w f ) ] n , ( 3 ) where C(t) represents a term which is a function of isochronal interval t. In the literature, methods are proposed to estimate the value of C corresponding to the stabilized rate based on the transient state information ?C(t) For example, Hinchman et al.1 propose that 1/C(t)1/n be plotted as a function of log t, and the line be extrapolated until t is equal to the time it takes to reach the stabilized state period. In their method, they assume that n is constant, where n is an inverse of slope when log[m(p¯)−m(pwf)] is plotted as a function of qg. Although we get different straight lines corresponding to different t, the authors assume that the slopes are approximately constant. Another commonly used approach in analyzing isochronal tests is to use an equation, m ( p  ̄ ) − m ( p w f ) = a g q g + b g q g 2 . ( 4 ) A similar equation can also be written in terms of pressure squared terms. Eq. 4 is derived starting from Forchheimer's equation. Under transient conditions, we can rewrite Eq. 4 as m ( p  ̄ ) − m ( p w f ) = a g ( t ) q g + b g q g 2 , ( 5 ) where ag(t) is a function of isochronal interval, and bg is assumed to be constant. A commonly used technique is to plot ag(t) vs. log (t) and extrapolate ag(t) corresponding to a value of t which represents the time required to reach a stabilized rate.2–4 In using both Eqs. 3 and 5, we have assumed that the contribution due to the non-Darcy effect is not affected during the transient conditions. For example, in applying Eq. 3, we assume that n is constant during the transient period, and in applying Eq. 5, we assume that bg is constant during the transient period. Both n and bg represent the relative contributions of the non-Darcy flow. n will approach 0.5 as the non-Darcy effect becomes dominant, and bg becomes larger as the non-Darcy effect becomes significant. However, by assuming that n and bg are constant during the transient periods, we are ignoring the changes in the relative contributions due to the Darcy and non-Darcy terms. In this article, we extend the previous analysis to account for changes in the non-Darcy term during the transient period. Further, by proper analysis, we propose a method to estimate the value of the turbulence coefficient based on the evaluation of the transient period data. Approach In our approach, instead of using the empirical equation (Eq. 3), we will begin with Forchheimer's equation, where the pressure gradient in a radial reservoir is calculated by ∂ p ∂ r = μ g k v + β ρ g v 2 . ( 6 ) The permeability (k) of the reservoir may be established based on well test data or core information. The turbulence coefficient is difficult to estimate. Although literature correlations5,6 exist to calculate the value of ? based on the laboratory experiments, field evidence7 indicates that the ? values in the field are significantly greater than the laboratory experiments.


2021 ◽  
Author(s):  
Emily Ako ◽  
Erasmus Nnanna ◽  
Odumodu Somtochukwu ◽  
Akinmade Moradeke

Abstract Chemical Sand Consolidation (SCON) has been used as a means of downhole sand control in Niger Delta since the early 70s. The countries where SCON has been used include Nigeria (Niger Delta), Gabon (Gamba) and UK (North Sea). SCON provides grain-to-grain cementation and locks formation fines in place through the process of adsorption of the sand grains and subsequent polymerization of the resin at elevated well temperatures. The polymerized resin serves to consolidate the surfaces of the sand grain while retaining permeability through the pore spaces. In a typical Niger Delta asset, over 30% of the wells may be completed with SCON. A high percentage are still producing without failure since installation from1970s. Where the original SCON jobs have failed, re-consolidation has also been carried out successfully. Chemical Sand Consolidation development has evolved over the years from: Eposand 112A and B, Eposand 212A and B, Wellfix 2000, Wellfix 3000, Sandstop (resin based), Sandtrap 225, 350 & 500 (resin based) and lately Sandtrap 225,350, 500 (solvent based) and Sandtrap ABC (aqueous based). There have been mixed results experienced with the deployment of either of the latest recipes of SCON. This was due to the fact that the conventional deployment work procedure was followed with the tendency for one-size-fits-all approach to the treatment. This paper details the challenges faced with sand production in ARAMU037, the previous interventions and how an integrated approach to the design and delivery of the most recent intervention restored the way to normal production. The well has now produced for about 2 years with minimal interruption with the activity paying out in less than 6 months. The paper also recommends the best practice for remedial sand control especially for wells in mature assets.


2021 ◽  
Author(s):  
Ahmed Alghamdi ◽  
Olakunle Ayoola ◽  
Khalid Mulhem ◽  
Mutlaq Otaibi ◽  
Abdulazeez Abdulraheem

Abstract Chokes are an integral part of production systems and are crucial surface equipment that faces rough conditions such as high-pressure drops and erosion due to solids. Predicting choke health is usually achieved by analyzing the relationship of choke size, pressure, and flow rate. In large-scale fields, this process requires extensive-time and effort using the conventional techniques. This paper presents a real-time proactive approach to detect choke wear utilizing production data integrated with AI analytics. Flowing parameters data were collected for more than 30 gas wells. These wells are producing gas with slight solids production from a high-pressure high-temperature field. In addition, these wells are equipped with a multi-stage choke system. The approach of determining choke wear relies on training the AI model on a dataset constructed by comparison of the choke valve rate of change with respect to a smoother slope of the production rate. If the rate of change is not within a tolerated range of divergence, an abnormal choke behavior is detected. The data set was divided into 70% for training and 30% for testing. Artificial Neural Network (ANN) was trained on data that has the following inputs: gas specific gravity, upstream & downstream pressure and temperature, and choke size. This ANN model achieved a correlation coefficient above 0.9 with an excellent prediction on the data points exhibiting normal or abnormal choke behaviors. Piloting this application on large fields, where manual analysis is often impractical, saves a substantial man-hour and generates significant cost-avoidance. Areas for improvement in such an application depends on equipping the ANN network with long-term production profile prediction abilities, such as water production, and this analysis relies on having an accurate reading from the venturi meters, which is often the case in single-phase flow. The application of this AI-driven analytics provides tremendous improvement for remote offshore production operations surveillance. The novel approach presented in this paper capitalizes on the AI analytics for estimating proactively detecting choke health conditions. The advantages of such a model are that it harnesses AI analytics to help operators improve asset integrity and production monitoring compliance. In addition, this approach can be expanded to estimate sand production as choke wear is a strong function of sand production.


1989 ◽  
Vol 7 (1) ◽  
pp. 51-62 ◽  
Author(s):  
M.A. Adelman

Governments seek to maximize their revenue from mineral resources without taking so much as to discourage investment. To achieve this, an understanding of the cost of production from any given property is necessary. This paper examines a number of proposed developments in the Norwegian North Sea and uses two methodologies to estimate costs in light of reported investment and production data. The results suggest that even $15/barrel will support development in all cases, though one field could be considered marginal, while $20/barrel makes the fields quite profitable, with rates of return ranging from 20% to 40%.


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