Reservoir modeling and the derived fluid production over time curves are a key part of the workflows associated with major capital project decisions. These models may be very complex and use a variety of geological constraints in an effort to develop the porosity, permeability, and saturation distributions used in dynamic models (with or without upscaling). Over time and partially in response to increased computing capability as well as the need for more realistically heterogeneous models, model size as measured by number of model cells and model complexity has increased but model-derived production forecasts remain optimistic. This paper, one of a series that now stretches back over a decade, addresses a number of modeling issues with the goal of (1) better understanding how modeling workflows may contribute to forecast optimism and (2) what reservoir modelers, both geologists and engineers, may do to reduce forecast optimism derived from their subsurface models by improved understanding of how model parameters such as grid size, number of grid cells, semivariogram parameters (e.g. the range), and number of geological/stratigraphic "control" surfaces used to constrain models. Adequate modeling of reservoir heterogeneity appears to require very to extremely large models (e.g. large number of small cells). Many of the parameters used to "control" heterogeneity including the semivariogram range parameter, the number of "detailed" stratigraphic layers, and the number of rock/facies "containers" or model regions appears to have only a small impact on forecast recovery.
The objective of the study is to determine the main mechanisms for sand production and to propose completion designs to minimize sand production for HPHT gas wells in the Tarim Basin. Sand production has been a very serious concern in these HTHP gas wells. This paper presents field results for several key wells which are prone to sanding and investigates the possible reasons and mechanisms responsible for sand production. A fully coupled 3D, poro-elasto-plastic sand production model has been developed and applied to study sand production issues for these wells. Sand production data from several wells were analyzed to better understand the conditions under which sand production occurs and conditions under which it is mitigated.
The sand production model was used to model the different completion designs and flow back strategies that were used in the field. The model couples multi-phase fluid flow and elasto-plasticity to simulate pressure transient and rock deformation during production. The sanding criterion is a combination of both mechanical failure (shear/tensile/compressive failure) and fluid erosion. A novel cell removal algorithm has been implemented to predict the dynamic (time dependent) sand production process. In addition, the complex geometry of the wells and perforations are explicitly modeled to show cavity propagation around hole/perforations during sand production.
For this study, triaxial tests on core samples have been conducted and the stress-strain curves under different confining stresses are analyzed to obtain rock properties for both the pre-yield and post-yield period. The wells were categorized into ones that had massive sand production and ones that showed much less sand production. Operational and mechanical factors that were empirically found to result in sand production were identified. The sand production model was run to verify the role played by different factors. It is shown that completion design, rock strength and post failure behavior of the rock are key factors responsible for the observed sanding in these wells. In addition, the drawdown strategy and the associated BHP change and the extent of depletion play an important role in the sanding rate. Several strategies for minimizing sand production are suggested for these wells. These include, drawdown management, completion and perforation design. In this study, we quantitatively show for the first time that data from HPHT gas wells that suffer severe sand production problems can be modeled and analyzed quantitatively to determine the mechanisms of sand production. This allows us to make operational recommendations to minimize sanding risk in these wells.
This study is based mainly on the Cubagua formation belonging to the Dragon field, where the intervals of interest of the deposits are poorly consolidated and the cementation of the grains of sand is poor, as to be able to withstand the efforts applied as a result of the passage of the produced fluids through them, being able to start the phenomenon of sandblasting. The realization of this work consisted of the use of the BP-Willson methodology
In order to get a full petrophysical evaluation from log-based traditional techniques in every location, the formation density is needed in wire-line log measurements; otherwise, with a limited amount of information in terms of porosity values, the reservoir characterization has more uncertainty. That is, the case study of the giant Bachaquero-02 reservoir, there is a lack of Rhob data in the spatial data sets that prevent a good assessment of the storage capacity in the petrophysical model and thus wrong original oil in place estimation. This paper, therefore, presents a solution to this problem; this work develops a methodology for predicting formation density values which establish a link between probabilistic interpretations from multi-mineral solution and deterministic predictions from multiple linear regression with the main objective of seeking a mathematical expression which describes the best fit for the Bachaquero Member and Laguna Member in each location. The manner of estimating formation density can vary according to the available data in well logs, as a first step, this technique uses classic lithology indicators from well logging such as gamma ray, spontaneous potential and resistivity index to calculate the most probable minerals in the rock with the purpose of assessing a probabilistic approach, the second stage is to create a prediction model with surrounding wells, the input data, which is the probabilistic outcome and measured logs, it is trained using a'least squares' regression routine that will find the best fit in the data for bulk density reckoning. A reliable formation density profile according to the lithology of the reservoir was obtained for each well. The model shows more than 0.9 of correlation coefficients between the density measured by wire-line services and the new bulk density reproduced in this method. Particularly, the Bachaquero-02 reservoir has a notorious heterogeneity along the stratigraphic column; the Bachaquero Member has different depositional environment and rock properties in comparison with Laguna Member which has poor quality reservoir rock. This workflow has the ability to incorporate reservoir heterogeneities in the probabilistic module without a problem. 2 SPE-191163-MS
Fines migration is a commonly observed phenomenon in oil and gas wells, but often difficult to duplicate in the laboratory. A suite of labs tests was conducted to gauge the effect that different test conditions have on fines migration and to improve fines migration prediction through updated test strategies. Core tests were conducted on core samples collected from a field in West Africa. Field B shows evidence of fines migration through increased PI and reduced skin after a diesel pump-in, and significant increase in production rates after hydrofluoric (HF) acid treatments. Some of the earlier conventional core tests conducted with cores from the same field failed to predict a potential for fines migration. Hence, a study to optimize current fines migration test methods was initiated. With the new tests, the effect of injected fluid volumes, injected fluid type, test temperature, surge conditions and depletion on fines was investigated. Results from these new tests showed evidence of fines migration as observed in the field, in contrast to the earlier tests conducted using conventional test methods. While the tests confirmed the presence of non-Darcy flow at high injection rates, strategies to exclude the contribution of non-Darcy flow from fines-related formation damage were developed and will be discussed.
According to EIA (
The complete evaluation methodology has 4 phases: 1) petrophysical evaluation, that includes multimineral evaluation, porosity estimation and calibration with mineralogical analysis; 2) TOC content evaluation, that includes TOC content estimation, using 3 methods: density logs
La Luna Formation and La Grita Member of Capacho Formation are mainly composed by carbonatic rocks, with high content of calcite (above 75%) and low content of clay minerals. In both units, the estimation of TOC content varies from 0.50 to 9%. Mechanical properties show moderate values of Poisson's ratio (0.20 to 0.32), high values of Young's modulus (0.80 to 9.60x 106 psi) and UCS (6.20 to 31.00x 106 psi). In the Cretaceous sequence, the state of stress changes according to geographic location in the basin, from normal in northwest region and central lake region, to transcurrent and reverse in southeast region. The brittleness index estimated for different methods varies from 0.54 to 0.85, which indicate that both units may be classify as brittle.
The integration of geomechanical and petrophysical analysis allowed identifying prospective intervals in both units, with thickness between 20 to 100 ft. Therefore, the study indicates that both units show very good conditions for horizontal drilling and hydraulic fracturing. Moreover, the comparison of various estimation methods of TOC content and brittleness index allowed to observe the uncertainty presented by these parameters in analysis of shale plays.
This paper presents the workflow and learned lessons during the construction of a fully compositional integrated subsurface/surface model for the Santa Barbara and Pirital fields, which are important oil production units located to the east of Venezuela. In this approach, the numerical reservoir simulation models, wells and surface facilities were coupled in order to obtain production profiles considering both changes in the reservoir conditions and surface restrictions, achieving an assertive planning of asset development.
The applied methodology is based on the construction of more than 150 compositional well models, performing sensitivity analysis to define multiphase flow correlations for vertical pipe and chokes. A network model, which comprises more than 900 Km of lines, 3 main flow stations, and 3 separation levels, was also built in compositional mode honoring line sizes, lengths and elevation changes. Two numerical simulation models represent the most reliable characterization of the main reservoirs. Each model was initialized and ran separately, in order to discard internal inconsistencies. Then, the integration was performed considering the sand face on the wells as the coupling point.
The integrated asset modeling allowed predicting the production behavior of the reservoirs taking into account the constraints of the surface facilities, reducing the uncertainty of forecasts and identifying limitations and bottlenecks at surface level. It was also possible to accurately determine the details of the hydrocarbons streams (NGL) at different pressure stages of the network, which reasonably matched with field data (less than 3% of difference). The result is a versatile tool for the integrated asset management, which allows to sensitize all the elements of the production chain and estimate how each one affect the performance of the asset, discarding the division between departments upstream and downstream and establishing a common management strategy for all disciplines.
The novelty of this work is based on the challenge of building fully compositional coupled models considering giants and complex reservoirs with large surface networks. The proposed methodology and learned lessons will certainly serve as reference for similar future works.
Water is the most commonly used injection fluid for flooding/energizing oil reservoirs. Despite oil price fluctuations, water use has continued because of its wide availability, relatively low cost, and ease of handling. Decades of research and field application experiences have yielded a sound theoretical approach and practical knowledge of the subject. Nevertheless, water injection deployment and operations can still benefit from optimization. This paper discusses the state-of-the-art use of numerical optimizers based on smart algorithms and stochastic machines that couple subsurface, surface, and economic models.
During planning and operations of waterflooding projects, many decisions are made, such as the number, location, and drilling sequence of new injector and producer wells, total and per well injection rates, well conversion, and fluid withdrawal rates. In addition, each decision variable has multiple options, which combined can generate hundreds or thousands of scenarios, raising the key question of how the optimum scenario can be determined in a timely manner. Furthermore, the optimum scenario selection process should consider uncertainty (e.g., reservoir properties and oil prices) as well as operational constrains.
Based on previous experience, a general workflow was developed and fine-tuned to help identify optimum scenarios. The workflow begins by defining the scenario matrix using available validated history-match models. Models are coupled with an automatic optimizer/stochastic machine. The study cases considered reservoirs with heavy-to-medium oil, injection by pattern and flank, large variations in original oil in place (OOIP), and number of wells for waterflooding implementation and reactivation planning.
Optimization runs typically require hundreds of iterations to approach the maximum or minimum objective business function. Each iteration corresponds to a scenario. To identify the optimal scenario quickly, various strategies were tested: parallel computing and new methodologies of sequential optimization with reduced number of decision variables, initial exploratory runs with a shortened economic horizon time, and stochastic analysis of selected scenarios of the optimization run. All of these strategies proved successful, depending on the specific situation.
The workflow application in three case studies yielded approximately 30% cumulative production and net present value (NPV) increments, with less economic risk than the traditional deterministic simulation approach and reduced water cut up to 40%; compared to base scenarios, Np and NPV increases higher than 200% were obtained. Furthermore, the workflow application generated a large number of scenarios that provided flexibility to modify operations during unexpected events.
Optimizers/stochastic machines were determined to be a valid means to quantitatively estimate the economy and risks and are a fundamental tool for managing waterflooding projects, resulting in better scenarios than the traditional deterministic approach. The approach is also applicable to all types of enhanced oil recovery (EOR) projects.
The EoceneFrac area corresponds to a subsoil extension of approximately 230 Km2, located northeast of Lake Maracaibo in the Lagunillas area, where Informal Members B-2-X and B-3-X have been defined as The main oil-producing sands of the Misoa Formation of the Eocene Age, classifying the B-2-X-68 deposit as the second at the western level in crude oil reserves and object of this study. The EoceneFrac reservoir is 24 ° API hydrocarbon and this reservoir is currently the second largest reservoir in the western region. Work on this site began in 1927 and is noted for the fact that it has to be fractured due to its low permeabilities, despite being a shallow production zone. The first fracture occurred in 1959 at the well Years later was the discoverer.
It has a significant consolidation, which aggravates the problem of the low fluidity of the hydrocarbons to the intermediations of the well, the deposit currently has forty-nine (49) wells of which are in different states being active sixteen (16), inactive six (6), abandoned seven (7), three (3) waiting for abandonment and finally seventeen (17) waiting for reconditioning works for the date of January 13, 2013, being the main problem affecting the production of these wells the low mobility.
A Geomechanical study focused on optimizing the fracture designs that arose due to the failed behaviors that had been previously carried out in the B2-X-68 deposit, ceased to function shortly after being made, it is noteworthy that they were found Important parameters of rock and proppant resistance that have a direct impact on collapse of the rock, as well as geopresiones that were not taken into account for initial designs.
A new workflow for fracture prediction and modelling based on geological time-step DFN has been used to better constrain the fracture distribution and timing of generation in the Motatan Domo Sur field of Venezuela. Using the Fault Response Modelling module in Move™, simulations of fracture generation under two tectonic transpressive events with SHmax of 280° and 310° were modeled to find the best-fit fracture forming event as compared with the observed data. These events are of Paleocene-Eocene and Miocene age, respectively. This workflow includes a DFN built from borehole images of five wells whose fracture properties are spatially modeled taken into account structural and petrophysical indicators of sub-surface fracture systems. A comparison between measured and modeled fractures is discussed to evaluate the influence of each tectonic event.
Modeling the location of discontinuities (faults and fractures) in the subsurface associated with a given tectonic event requires a geomechanical model, which incorporates stress boundary conditions and mechanical properties. In this paper, we outline a new workflow which allows fracture forming events to be simulated and used to predict fracture distributions across reservoirs; the results of these simulations can supplement petrophysical, geomechanical and subseismic indicators to produce more representative fracture models. This workflow is applied to the south dome of the Motatan reservoir, which is located in the tectonically complex Maracaibo basin of Venezuela.
This workflow consists of two phases: 1) the building of present day discrete fracture network (DFN) through the integration of petrophysical, geomechanical, structural and subseismic indicators of fracture systems (e.g. curvature and bore hole image data); and 2) the simulation of slip on faults, calculating the resulting strain field and comparing predicted fracture orientations for different tectonic events with the observed fractures. The combination of these two phases provides a better understanding of natural fracture systems and provides information about the development of reservoir fracture systems through time.