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Porosity cut-off is a major key parameter in evaluation of reservoir dimensions. Through the use of this parameter formation rocks regarded as ‘reservoir(s)’ are distinguished from ‘non-reservoir’ ones. This makes determination of this parameter to have a direct impact on estimations of reservoir volumes and in turn volumes of hydrocarbon accumulation. Error in the determination may influence commerciality of the hydrocarbon accumulation. Traditionally, determination of porosity cut-off values is made through relationship between rock porosity and permeability. Although some suggested the use of supporting data such as clay volume and porosity types or computational methods such as artificial neural network, this method is often regarded as unsatisfactory due to different intrinsic features between the two. Relationship between rock pore throat size – obtained from mercury injection on core samples – and permeability is on the other hand more direct in nature. This study actually attempts to integrate this more theoretically consistent relationship into the old porosity – permeability relationship. The integration is essentially made through multi-variable regression analysis using data from eight (4 sandstone and 4 limestone) oil and gas fields in Indonesia. Results from the study have shown significant improvement in porosity – permeability relationship resulting in more reliable porosity cut-off estimates. The method, which procedure is presented in this article is hoped to have helped petrophysicists in reducing and overcoming high uncertainties in estimating porosity cutoff.
Porosity cut-off is a major key parameter in evaluation of reservoir dimensions. Through the use of this parameter formation rocks regarded as ‘reservoir(s)’ are distinguished from ‘non-reservoir’ ones. This makes determination of this parameter to have a direct impact on estimations of reservoir volumes and in turn volumes of hydrocarbon accumulation. Error in the determination may influence commerciality of the hydrocarbon accumulation. Traditionally, determination of porosity cut-off values is made through relationship between rock porosity and permeability. Although some suggested the use of supporting data such as clay volume and porosity types or computational methods such as artificial neural network, this method is often regarded as unsatisfactory due to different intrinsic features between the two. Relationship between rock pore throat size – obtained from mercury injection on core samples – and permeability is on the other hand more direct in nature. This study actually attempts to integrate this more theoretically consistent relationship into the old porosity – permeability relationship. The integration is essentially made through multi-variable regression analysis using data from eight (4 sandstone and 4 limestone) oil and gas fields in Indonesia. Results from the study have shown significant improvement in porosity – permeability relationship resulting in more reliable porosity cut-off estimates. The method, which procedure is presented in this article is hoped to have helped petrophysicists in reducing and overcoming high uncertainties in estimating porosity cutoff.
We propose a novel concept relating to using net-to-gross (NTG) ratios in characterizing hydraulic fracture deliverability. Current NTG considerations applied in unconventional resources are primarily petrophysics-based. However, the variability of in-situ stress and material properties in the vertical direction greatly influence placement and vertical connectivity of a given hydraulic fracture. Hence, the areas of low connectivity within the created fracture system, such as pinch offs, can impact production performance and thus become a key asset development driver that ultimately influences project economics. Therefore, characterizing pinch offs in a created fracture network becomes a necessity. Such characterization can be accomplished by splitting NTG into two components—petrophysical (NTG-P) and stimulation (NTG-S). The focus of this work is the NTG-S. The proposed NTG-S is currently based on hydraulic fracture and production models coupled with calibration data from offset wells. For a given stress and material property profile, hydraulic fracture geometries are estimated using a fracture simulator with multiple calibration points. The post fracture closure width profile from the calibrated fracture model indicates possible pinch points along the created fracture height. The calibrated fracture geometry is then integrated into a production model, where the impact of the pinch points on well performance is simulated while honoring the actual production data. High-resolution stress logs and fullbore cores can help to address uncertainty in location and severity of pinch points and aid in reducing the non-uniqueness that is inherent to production models. NTG-S influences the productive fracture height, which in turn impacts the drainage volume and drainage area around a well. Subsequently, the drainage area affects well spacing and completion design considerations. For a given productive fracture surface area, overestimating NTG-S (assuming a large productive fracture height) can result in an assumed short drainage length, leading operators to laterally place offset wells closer than needed. Conversely, underestimating NTG-S can cause operators to space offset wells too far apart, resulting in lower recoveries. Consequently, in very thick unconventional reservoirs, stacked horizontal wells may be more appropriate. Accounting for NTG-S allows optimizing lateral landing points to ensure optimum drainage configuration is achieved.
Approximately 30 relative permeability modifier (RPM) fracturing treatments performed in Colombia will help establish consistent, reliable best practices for future applications. Some of these treatments combined scaling inhibition stages to maintain production enhancement, in spite of high or medium formation water scaling tendencies. Because the water-injection system is significantly dynamic in these fields, new injection water channels were communicated with producer wells after RPM-fracturing treatments, reducing scaling inhibition requirements and RPM effectiveness for water production management. The objective of combining RPM and fracturing treatments was to increase oil production over time through effective water production control techniques. Evaluations of the effectiveness of both general and specific treatment processes and service are available in the industry. However, data for the analysis of RPM-fracturing treatments presented in this paper was obtained in two basic procedures:The workover operating efficiency (WOE) index was established to evaluate fracturing effectiveness (total fluid production increase) and RPM effectiveness (water-oil ratio [WOR] reduction). The WOE index quickly analyzes all treatments and identifies the best-performing ones based on two factors:production-related WOE indexes higher than one, which is the maximum production rate after treatment vs. the minimum production rate before treatment, andWOR-related indexes lower than one, or the minimum WOR after treatment vs. the maximum WOR before treatment.This analysis can quickly define the best and worst performances by evaluating individual fracturing effectiveness and individual RPM effectiveness.The reservoir-performance analysis is a cyclical and detailed process that does not focus on individual wells but rather analyzes the entire reservoir. This stage consists of an extensive analysis that involves reservoir characterization, well testing and production history, completion design, stimulation operation design, and stimulation operation execution and feedback. Because this analysis was so extensive, it was applied only to the best- and worst-case scenarios (two different fields) in which combined RPM-fracturing/scaling inhibition treatments had been in effect for more than 2 years. Those two fields presented some similarities in terms of producing-zone depth, reservoir character, reservoir fluid properties, and water-injection systems. Both general and specific processes for treating and servicing wells have been evaluated for effectiveness by the industry. Applying the results of the two analyses presented in this paper, best practices are now defined for future well selection, treatment design, and operation execution for combined RPM-fracturing/scaling inhibition treatments. Introduction An average of 15 RPM-fracturing treatments for each of two fields was evaluated for this study. These fields, identified as Fields A and B, exhibit many similarities and only a few differences. Both are sandstone, producing formations with typical average clay minerals content. Producing zones are average with similar depths and bottomhole temperatures. The collection, review, and analysis of these jobs has been sufficient for defining best practices for future jobs on similar wells. A detailed analysis of the entire reservoir and field was performed for two scenarios: the best and the worst case. These cases were defined according to:the highest production increase ratio as a moving average during 3 months,the highest incremental production after 6 months, andthe lowest water-oil ratio decrease as a moving average during 3 months.1 Additional details and concepts for the two basic analysis procedures based on well performance are provided in the following sections.
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