Se describe la evolución de las normas sísmicas en El Salvador, incluyendo un enfoque probabil ística en la norma vigente. Se compara esta última norma con tres trabajos anteriores y se atribuyen las discrepancias a diferencias en cada paso de la estimación y no únicamente en las relaciones de atenuación. Se discuten las atenuaciones espectrales para América Central.
The Oriente Basin is located in eastern Ecuador at the Amazon rainforest. Shushufindi-Aguarico field is one of the most important fields in Oriente Basin with over 12% of the national production; the main hydrocarbon reservoirs are located inside the Cretaceous formations Napo and Tena. In spite of being a mature field in production since the beginning of 1970s, Shushufindi-Aguarico field still presents various formation evaluation challenges that can potentially be explored to enhance its productivity. In order to improve fluids characterization in a recently developed area at NorthWest of the field, a new reservoir evaluation technology, Fluid Logging and Analysis in Real Time, is introduced to obtain a continuous log of quantitative composition of hydrocarbon and an improving in the pay zones analysis from gas presence in the mud while drilling. The prospective intervals determination within the productive reservoirs is performed while drilling with cuttings analysis and chromatography evaluation in real time. This evaluation is based on Gas Ratio Method, which uses the relation between heavy, medium and light gases to identify porous rocks with hydrocarbon presence. The prospective intervals determination using Advanced Surface Fluid Logging technology gives more precision to identify thin beds by eliminating the recycled gas effect than conventional mud logging. In addition, the Advanced Surface Fluid Logging provides fluid composition in the C1-C5 range analogous to the PVT single phase composition. The fluid composition achieved in the main target zone exhibited a close correlation with a convention PVT from a recent offset well. This paper presents a case study where ASFL technology was tested on a Shushufindi well highlighting valuable benefits, with better pay zones definition in the challenging geological environments encountered in the Shushufindi-Aguarico field. The reliability of the data provided is demonstrated by the good correlation amongst the Fluid Logging and Analysis in Real Time composition recorded in the main target zone and a recent PVT composition from a nearby offset well.
The Shushufindi field is located in the Oriente basin of Ecuador. The field was discovered in 1972 and widely developed with about 247 wells covering an area of approximately 400 km 2 . The implementation of lithofacies characterization in 98% of the existing wells has given a reliable description in about 92% of the wells in the current geomodel, which demonstrates, the validity of the deterministic method.A robust petrophysical rock type (PRT) classification can significantly improve the chances of success for all wells, focusing on layered reservoir rocks recognized as the major energy resource in recent years. The vertical and lateral classification of rock heterogeneity in the form of rock types is critical to understand the flow dynamics of the reservoirs. Well logs are the best option for formation evaluation as they provide high vertical resolution measurements. However, rock type's classification using only well logs interpretation techniques, has its limits.In this paper, we introduce a rock type neural network technique based on Indexed and Probabilistic Self-Organized Mapping (IPSOM) which was designed for the geological interpretation of well log data, facies prediction and optimal derivation of petrophysical parameters. The rock typing was based on cored wells in a 3-step approach. Preliminary rock type identification was based on sedimentology description and routine core analysis. In parallel, it was refined with high pressure mercury injection data to describe accurately the porous media. The porosity and permeability ranges were established to elaborate a sand facies classification represented by Petrophysical Rock Type through Winland method. The neural network was first trained on cored reservoirs, and then propagated to uncored wells using the classification model relationship with electrical logs. Finally using the IPSOM classification model, a permeabilityporosity relationship for each rock type was obtained, providing input to the dynamic model to predict and validate permeability. This paper present a reservoir characterization enhancement technique using neural network, which has proven its utility in refining the dynamic model of the Shushufindi field and directly contributing to the operator by improving production from layered reservoirs.
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