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In today's dynamically challenging E&P industry, exploration activities demand for out-of-the-box measures to make the most out of the data available at hand. Instead of relying on time consuming and cost-intensive deliverability testing, there is a strong push to extract maximum possible information from time- and cost-efficient wireline formation testers in combination with other openhole logs to get critical reservoir insight. Consequently, driving efficiency in the appraisal process by reducing redundant expenditures linked with reservoir evaluation. Employing a data-driven approach, this paper addresses the need to build single-well analytical models that combines knowledge of core data, petrophysical evaluation and reservoir fluid properties. Resultantly, predictive analysis using cognitive processes to determine multilayer productivity for an exploratory well is achieved. Single Well Predictive Modeling (SWPM) workflow is developed for this case which utilizes plethora of formation evaluation information which traditionally resides across siloed disciplines. A tailor-made workflow has been implemented which goes beyond the conventional formation tester deliverables while incorporating PVT and numerical simulation methodologies. Stage one involved reservoir characterization utilizing Interval Pressure Transient Testing (IPTT) done through the mini-DST operation on wireline formation tester. Stage two concerns the use of analytical modeling to yield exact solution to an approximate problem whose end-product is an estimate of the Absolute Open Flow Potential (AOFP). Stage three involves utilizing fluid properties from downhole fluid samples and integrating with core, OH logs, and IPTT answer products to yield a calibrated SWPM model, which includes development of a 1D petrophysical model. Additionally, this stage produces a 3D simulation model to yield a reservoir production performance deliverable which considers variable rock typing through neural network analysis. Ultimately, stage four combines the preceding analysis to develop a wellbore production model which aids in optimizing completion strategies. The application of this data-driven and cognitive technique has helped the operator in evaluating the potential of the reservoir early-on to aid in the decision-making process for further investments. An exhaustive workflow is in place that can be adopted for informed reservoir deliverability modeling in case of early well-life evaluations.
In today's dynamically challenging E&P industry, exploration activities demand for out-of-the-box measures to make the most out of the data available at hand. Instead of relying on time consuming and cost-intensive deliverability testing, there is a strong push to extract maximum possible information from time- and cost-efficient wireline formation testers in combination with other openhole logs to get critical reservoir insight. Consequently, driving efficiency in the appraisal process by reducing redundant expenditures linked with reservoir evaluation. Employing a data-driven approach, this paper addresses the need to build single-well analytical models that combines knowledge of core data, petrophysical evaluation and reservoir fluid properties. Resultantly, predictive analysis using cognitive processes to determine multilayer productivity for an exploratory well is achieved. Single Well Predictive Modeling (SWPM) workflow is developed for this case which utilizes plethora of formation evaluation information which traditionally resides across siloed disciplines. A tailor-made workflow has been implemented which goes beyond the conventional formation tester deliverables while incorporating PVT and numerical simulation methodologies. Stage one involved reservoir characterization utilizing Interval Pressure Transient Testing (IPTT) done through the mini-DST operation on wireline formation tester. Stage two concerns the use of analytical modeling to yield exact solution to an approximate problem whose end-product is an estimate of the Absolute Open Flow Potential (AOFP). Stage three involves utilizing fluid properties from downhole fluid samples and integrating with core, OH logs, and IPTT answer products to yield a calibrated SWPM model, which includes development of a 1D petrophysical model. Additionally, this stage produces a 3D simulation model to yield a reservoir production performance deliverable which considers variable rock typing through neural network analysis. Ultimately, stage four combines the preceding analysis to develop a wellbore production model which aids in optimizing completion strategies. The application of this data-driven and cognitive technique has helped the operator in evaluating the potential of the reservoir early-on to aid in the decision-making process for further investments. An exhaustive workflow is in place that can be adopted for informed reservoir deliverability modeling in case of early well-life evaluations.
Representative fluid properties are required for a wide range of field life aspects such as initial sizing of reservoir hydrocarbon reserves and production planning. Fluid properties are routinely obtained from laboratory sample analysis, but some fluid properties can also be measured in situ with formation testers. A new downhole bubblepoint technique has been developed to supplement traditional downhole fluid analysis measurements. Bubble-initiation pressure is measured on reservoir fluids enabling early estimations and sample representativity. The method outlined consists of two parts: bubble generation and bubblepoint-pressure detection. After isolation of a volume of contamination-free fluid in the fluid analyzer module of a formation tester, a downhole pump is used to reduce flowline pressure at a low and precise flow rate. Bubble initiation is detected using optical spectroscopy measurements made at a 64-ms data sampling rate. Even very small bubbles scatter visible and near-infrared light directed through the flowline, ensuring that the initiation of bubbles is detected. Flowline decompression experiments are performed in minutes, at any time, and on a wide range of downhole fluids. Downhole bubblepoint pressure measurements were made on four different fluids, all from different reservoirs and regions. The gas-oil ratio of the tested fluids ranged from 500 to 1,500 scf/bbl. In each case, the downhole bubblepoint obtained from the flowline decompression experiment matched the saturation determined by constant composition expansion in the laboratory to within 50 psi. We observed that bubble initiation is first detected using near-infrared spectroscopy. As pressure drops, gas bubbles coming out of solution will increase in size, and the bubble presence becomes identifiable on other downhole sensors such as the live fluid density and fluorescence, where it manifests as signal scattering. For each of the investigated fluids, pressure and density measurements acquired while the flowline pressure is above saturation pressure are also used to compute compressibility as a function of pressure. This downhole bubblepoint pressure measurement allows optimizing real-time sampling operations, enables fluid grading and compartmentalization studies, and can be used for an early elaboration of a fluid equation of state model. The technique is well-suited for black oils and volatile oils. For heavy oil with very low gas content, the accuracy of this technique may be reduced due to the energy required to overcome the nucleation barrier. Prior documented techniques often inferred downhole bubblepoints from analysis of the rate of change of flowline pressure. Direct precise detection of the onset of gas bubble appearance without the need to divert fluid flow is shown for the first time on a wide range of fluids. The measurement accuracy is enabled by the combination of 64-ms optical spectroscopy with low and accurate decompression rates.
Summary Representative fluid properties are required for a wide range of field life aspects such as initial sizing of reservoir hydrocarbon reserves and production planning. Fluid properties are routinely obtained from laboratory sample analysis, but some fluid properties can also be measured in situ with formation testers. A new downhole bubblepoint technique has been developed to supplement traditional downhole fluid analysis (DFA) measurements. Bubble-initiation pressure is measured on reservoir fluids enabling early estimations and sample representativity. The method outlined consists of two parts—bubble generation and bubblepoint-pressure detection. After the isolation of a volume of contamination-free fluid in the fluid analyzer module of a formation tester, a downhole pump is used to reduce flowline pressure at a low and precise flow rate. Bubble initiation is detected using optical spectroscopy measurements made at a 128-ms data sampling rate. Even very small bubbles scatter visible and near-infrared light directed through the flowline, ensuring that the initiation of bubbles is detected. Flowline decompression experiments are performed in minutes, at any time, and on a range of downhole fluids. Downhole bubblepoint pressure measurements were made on four different fluids. The gas/oil ratio (GOR) of the tested fluids ranged from 90 m3/m3 to 250 m3/m3. In each case, the downhole bubblepoint obtained from the flowline decompression experiment matched the saturation determined by constant composition expansion (CCE) in the laboratory to within 350 kPa. We observed that bubble initiation is first detected using near-infrared spectroscopy. As the pressure drops, gas bubbles coming out of the solution increase in size, and the bubble presence becomes identifiable on other downhole sensors such as the live fluid density and fluorescence, where it manifests as signal scattering. For each of the investigated fluids, pressure and density measurements acquired while the flowline pressure is above saturation pressure are also used to compute compressibility as a function of pressure. This downhole bubblepoint pressure measurement allows optimization of real-time sampling operations, enables fluid grading and compartmentalization studies, and can be used for an early elaboration of a fluid equation-of-state (EOS) model. The technique is suitable for black oils and volatile oils. For heavy oil with very low gas content, the accuracy of this technique may be reduced because of the energy required to overcome the nucleation barrier. Prior documented techniques often inferred downhole bubblepoints from the analysis of the rate of change of flowline pressure. Direct detection of the onset of gas bubble appearance without requiring additional dedicated downhole equipment and validated against laboratory measurements is shown for the first time. The measurement accuracy is enabled by the combination of 128-ms optical spectroscopy with low and accurate decompression rates.
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