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.
As most oilfields in Ecuador are approaching to the end of the service contracts under an advanced degree of maturity, it was imperative to implement a fast-track integrated methodology that supports the decisionmaking process during assets' evaluation. This practice aimed to identify new business opportunities and assure the rehabilitation of brownfields. These fields became a target for investors willing to intervene in new joint ventures with moderate risk to boost production and returns. The methodology is prepared to overcome specific challenges such as severe reservoir pressure depletion, harsh water management issues, facilities constraints and integrity. All this while keeping economics and safe operational standards. This process is divided into five stages: First, the diagnosis of field challenges and associated risks, so that review the current status of subsurface and surface aspects. Then, the following three parallel phases are focused on the study of reservoir architecture, dynamics and performance. Finally, the remaining potential of the asset is assessed by integrating action plans to take advantage of current facilities capacities. This workflow was implemented for the evaluation of three assets: Asset 1: Mature field with a secondary gas cap where its current reservoir pressure is 800 psia (initial pressure 4,200 psia). The asset was evaluated in fifteen (15) days resulting in an integrated solution with 14 activities: conversions to injectors, water source, upsizing, reactivations, change zone, and new wells. The results presented an incremental recovery factor of 6% (by 2028) with an expected production peak of 3,500 BOPD (by 2021). Asset 2: A field producing from two main reservoirs with harsh water management issues under a non-monitored waterflooding scheme with challenging sweet spots identification was evaluated in 10 days, resulting in a redevelopment plan considering: production losses optimization, sixteen (16) activities: workovers, dual completions, new wells, reentry, shut-in, and conversion to water injectors. This evaluation delivered an incremental recovery factor of 10% (by 2029). Asset 3: Producing for around one-hundred (100) years with 3,000 wells drilled. There was a lack of pressure support and facilities and well completions integrity. The fast-track assessment focused on production optimization lasted fifteen (15) days, resulting in one-hundred eighteen (118) wells for reactivation representing an additional recovery factor of 3% (by 2029). This work supported the process for contract's renegotiation and assets' acquisition. This integrated methodology aimed to maximize the assets' value while considering the involved shareholders' needs. Each asset was analysed in an integrated and collaborative manner through the propper resources identification and the usage of the latest technology and workflows. High-resolution reservoir simulation, complex python scripts, and a chemical processes simulator were used to perform an in-depth evaluation and meet the expectations.
In 2010, Petrolera Indovenezolana S.A. (PIV), a joint venture between Corporación Venezolana del Petróleo (CVP) and the Indian company ONGC Videsh Limited (OVL), started planning for two horizontal wells in the Norte Zuata (San Cristóbal) field in the Orinoco belt of eastern Venezuela. The focus for this campaign was to evaluate the productivity of horizontal wells in thin sands and avoid areas of complex geology because of the high uncertainty in the structural behavior, applying technology that provided absolute control of the drilling process into the Oficina formation. Within the Oficina formation, thin sand reservoirs with variations in thickness and dip, geologically facies changes and subseismic faults presented the main challenge to geosteering a horizontal well. Because the project faced high geological uncertainties, a pilot hole was drilled as the first stage in the first well to verify the structural levels and the continuity of the sand bodies. To achieve the above challenges, the combination of a rotary steerable system (RSS) "point-the-bit" and a deep azimuthal electromagnetic resistivity tool (DAEMR) was used. The measurements provided accurate information to the well-placement engineers for proactive decisions in real time, mitigating the possible loss of the target by these geologic uncertainties. High-quality and valuable data for real time geological model update were the expected results obtained from the effort made by PIV in the Norte Zuata (San Cristóbal) field, and the data showed the oil-producing potential of one of the main reservoir (Sand F, G). This application of high-tier technologies demonstrated that drilling and data measurements can be improved and optimized to yield added value for reservoir development and 100% net to gross (NTG) targets. This reduces operational cost, makes it possible to drill in the right place the first time, and pushes forward the limit of the achievable in terms of reservoir exposure.
The main aim of geological modeling is to include all available data, such as seismic data, well logs, and core data, and to combine these data with more descriptive information, such as the geoscientist's understanding of the conceptual geological model and their experiences in similar environments, to predict the reservoir properties between the wells. However, in many cases, when the static model is passed to the reservoir engineer for history matching, the detailed geological knowledge and uncertainty is not fully utilized. This can lead to a model that may match the production data but actually has very little predictive power.Depositional maps provide very useful constraints on model building. By giving a visual representation of the geological context, they can incorporate well and seismic information and the dynamic characteristics recognized from the production data, tracer information in cases of water injection, and pressure information. However, there always remains a degree of uncertainty with respect to the geometries and orientations of the geobodies, so the tuning of the maps is, by definition, an iterative process. This coupling between static and dynamic modeling is critical to achieve true discipline integration, aiming to retain the key information from each domain. This paper presents an iterative technique to update these depositional maps in the areas of uncertainty between the wells. The required changes to honor production data and reservoir pressure trends during the history match are translated into facies modifications that are validated in terms of being consistent with both the control well data and with the conceptual depositional model. This methodology was applied to the modeling of Shushufindi field, Ecuador. The long-term field development plan is being guided by a fine-scaled geocellular model that was designed to capture the geology at a high resolution. The workflow adopted required the collaborative efforts of the geology, geophysics, petrophysics, and reservoir engineering team members throughout the entire process.
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