Use Conventional and Statistical Methods for Porosity Estimating in Carbonate Reservoir in Southern Iraq, Case Study

Porosity is important because it reflects the presence of oil reserves. Hence, the number of underground reserves and a direct influence on the essential petrophysical parameters, such as permeability and saturation, are related to connected pores. Also, the selection of perforation interval and recommended drilling additional infill wells. For the estimation two distinct methods are used to obtain the results: the first method is based on conventional equations that utilize porosity logs. In contrast, the second approach relies on statistical methods based on making matrices dependent on rock and fluid composition and solving the equations (matrices) instantaneously. In which records have entered as equations, and the matrix is solved in one step, the porosity, saturation, and volume of minerals embedded inside the rock formations were obtained. The results indicated that the porosity was determined using statistical and conventional approaches matched to the core porosity. In the end, statistical techniques afford a different path for calculation and provide outcomes that can be used in all situations, particularly when the rock has many types of components. Furthermore, it is not based on conventional equations and overcomes the problems coming from the unreliability of porosity logs in formations containing mixed minerals.

Discriminating Shale Layers by Pseudo CGR Logs Created Using Artificial Intelligence

The discrimination of shale vs. non-shale layers significantly influences the quality of reservoir geological model. In this study, a novel approach was implemented to enhance the model by creating Pseudo Corrected Gamma Ray (CGR) logs using Artificial Intelligence methods to identify the thin shale beds within the reservoir. The lithology of the carbonate reservoir understudy is mostly composed of dolomite and limestone rock with minor amounts of anhydrite and thin shale layers. The identification of shale layers is challenging because of the nature of such reservoirs. The high organic content of the shales and the presence of dolomites, particularly the floatstones and rudstones, can adversely affect the log quality and interpretation and may result in inaccurate log correlations, overestimating/ underestimating Original Oil In Place (OOIP) and reservoir net pays. In such cases, Corrected Gamma Ray (CGR) curves are typically used to identify shale layers. The CGR curve response is due to the combination of thorium and potassium that is associated with the clay content. The difference between the total GR and the CGR is essentially the amount of uranium-associated organic matter. Because of the very limited number of CGR logs in this reservoir, Artificial Intelligence (AI) approach was used to identify shale volume across the entire reservoir. Synthetic CGR curves were generated for the wells lacking CGR logs using AI methods. Resistivity, Density, Neutron and total GR logs were used as inputs while CGR was set as the target. Five wells that have CGR logs were used to train the model. The created pseudo logs were then used to identify shale layers and could also be used to correct effective porosity logs. After statistical analysis of the data, two different Artificial Intelligence Techniques were tested to predict CGR logs; Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). A Sugeno-type FIS structure using subtractive clustering demonstrated the best prediction with correlation coefficient of 0.96 and mean absolute percentage error (MAPE) of 20%. The resulting synthetic CGR curves helped identify shale layers that do not extend over the entire reservoir area and ultimately correct the effective porosity logs in the reservoir model. Porosity was primarily obtained from the neutron-density logs which results in very high porosity measurements across the shale layers. This study shows a new workflow to predict shale layers in Carbonate reservoirs. The created pseudo CGR logs would help predict shale and is an added-value data that could be incorporated into the Earth model.

Study of Petrophysical Properties of the Yamama Formation in Siba Oilfield

Evaluating a reservoir to looking for hydrocarbon bearing zones, by determining the petrophysical properties in two wells of the Yamama Formation in Siba field using Schlumberger Techlog software. Three porosity logs were used to identify lithology using MN and MID cross plots. Shale volume were calculated using gamma ray log in well Sb-6ST1 and corrected gamma ray in well Sb-5B. Sonic log was used to calculate porosity in bad hole intervals while from density log at in-gauge intervals. Moreover, water saturation was computed from the modified Simandoux equation and compared to the Archie equation. Finally, Permeability was estimated using a flow zone indicator. The results show that the Yamama Formation is found to be mainly limestone that confirmed by cuttings description and this lithology intermixed with some dolomite, in addition to gas and secondary porosity effects. Generally, the formation is considered clean due to the low shale volume in both wells with the elimination of the uranium effect in well Sb-5B. The calculated porosity was validated by core porosity in YC and YD units. Modified Simandoux gives a better estimation than the Archie equation since it takes into account the conductive of matrix in addition to the fluid conductivity. Five equations were obtained from porosity permeability relationship of core data based on five hydraulic flow units reorganized from the cross plot of reservoir quality index against normalized porosity index. The overall interpretation showed that YC and YD units are the best quality hydrocarbon units in the Yamama Formation, while YA came in the second importance and has properties better than YB. Moreover, YE and YFG are poor units due to high water saturation.

Evaluation of Reservoir Fluids Mobility, Mauddud Reservoir (Albian), Khabbaz Oil Field, Kirkuk Area, NE Iraq

The Albian Mauddud reservoir of the Khabbaz Oil Field is consisting of 170 m alternating shelf carbonates and pervasive dolomite horizons of coarse to fine crystalline mosaic. Core analysis and log measurements reveal the occurrence of three electrofacies units (A, B, and C) with variable petrophysical properties. Unit A with good reservoir quality shows average porosity of 18.8 % and average permeability of 27.5 md. The other two units (B and C) are less attractive and have an average porosity of 9.6 % and 9.2 % consequently. Pore size ranges between macro to meso types and related mainly to vugs, fractures and intercrystalline porosity, especially in the dolomite units. The reservoir fluids saturation, bulk volume, and mobility are evaluated using resistivity logs measurements and porosity logs (Neutron-Density porosities) in addition to other reservoir laboratory data. Calculations and cross data plotting of the related petrophysical parameters were applied to the three units of the Mauddud reservoir in seven wells of the field. It shows an overall good reservoir fluids mobility. Results indicate that the formation water of Khabbaz Oil Feld is a non-movable type especially for the crestal wells which make most of these wells produce water-free hydrocarbon. Variability within well’s hydrocarbon mobility is noticed and related to units and subunits lithology and porosity variation. Other variations seem to be related mainly to permeability, pores geometry and variability of water saturation in addition to the location of well with respect to oil pool within the field structure.

An Innovative Approach to Integrated Fluid Typing in Depleted Reservoirs

The economic value of completing a reservoir is strongly influenced by the fluid type. Wells drilled in developed brown field penetrate reservoirs with significant pressure loss due to offset production. A major challenge in evaluating mature reservoirs is the uncertainty introduced by pore fluids with unknown or varying petrophysical properties, such as change hydrocarbon gravity, diminishing pore pressures, and low to absent gas level indication. These are prone to error and uncertainty. Accurate understanding of reservoir fluid properties is therefore a key requirement for successful reservoir management. This manuscript illustrates a successful integrated workflow to ascertain. An integration between LWD triple combo data, near/far neutron, mud logs, pressure measurement, and production history of neighbouring wells, are critical to confirm fluid type within the drilled reservoirs. Cross plots, ratios and confidence analysis are required to ascertain the confidence level. Acquired data was ranked according to uncertainty associated with the acquisition technique, rate of penetration, lag time, mud type, and pre-test drawdown. Mobility was used as an indicator of fluid type or phase change in absence of any major rock type changes. Gas data were verified for any mud contamination and analysed using ratios to verify Hydrocarbon wetness. Data was ranked based on confidence factor determined through data precision and reservoir propertied. We also highlight the uncertainty in measurements. The fluid typing workflow used successfully identified the correct fluid typing, and reduced the reliance on single conventional method, or the need to run pre-test measurements. Data in intervals dominated with residual oil saturation showed misleading fluid type, same applies in high permeability sand, corrected gas data analysis gave a good indication of fluid type and mapped the change in fluid phase when combined with log data, while near/ far neutron aided to correlate the different sands, however due to its relationship with porosity, there is no one correlation could be derived. This paper illustrates that standard petrophysical techniques, such as analysis of density and neutron porosity logs, near/far neutrons, pretest can give misleading results if used in solo without consideration to the uncertainty associated with the measurement. The integration of fundamentally different data has resulted in identifying the fluid typing and its distribution in the reservoir and without integrating other measurements. A fluid typing systematic was developed to ensure the best and cost-effective model to assure the correct fluid type is identified. In this paper, a methodology is proposed which uses the geodesic transform, and integrate various source fundamentally different data, which is routinely acquired, then develop a systematic reasoning of confidence on data precision and accuracy. The system followed ensured the correct mapping of fluid typing in various reservoirs with different petrophysical properties. It is the first time such workflow is followed, and an integrated approach is consistently used in different sandstone reservoirs.

Comparison of Different Logging Techniques for Porosity Determination to Evaluate water Saturation

The purpose of this work is to evaluate thesaturation profile of Zubair formation in East Baghdad Field, from log interpretation.The relative accuracy of four methods of porosity evaluation, from three porosity logs (Neutron, Density and sonic logs ) , was tested against measured porosity from cores of eighty nine chosen intervals.The accuracy of each method was evaluatedstatistically by calculating the correlation coefficient, standard deviation error, average percentage error and absolute average percentage error.The crossplot technique (using triangle method ) of Neutron and Density logs data was found to give the best statistical parameters, hence, it was used to calculate optimum porosity values.It is concluded that the adoption of this method in porosity determination will result in more accurate water saturation determination particularly when using Archie's equation.

Characteristics of the Oil Water Contact Zone of Nhar Umr and Mishrif Reservoirs in Kumait and Dujaila Oil Fields, Southern Iraq, Using Vp/Vs Ratio and Porosity Logs Data

The detailed data of the Vp/Vs ratio and porosity logs were used to detect the Oil-Water Contact Zone (OWCZ) of Nahr Umr sandstone and Mishrif limestone reservoir formations in Kumiat (Kt) and Dujaila (Du) oil fields, southeastern Iraq. The results of OWC were confirmed using P-wave, Resistivity, and Water Saturation (Sw) logs of Kt-1 and Du-1 wells. It was found that the values of the oil-water contact zone thickness in Nahr Umr sandstone and Mishrif limestone were approximately one meter and eight meters, respectively. These results suggest that the OWCZ is possibly thicker in the carbonate rock than clastic rock formations. The thickness of OWCZ in the clastic rocks changed from one part to another, depending on several factors including mineral composition, grain size, porosity, pore shape, and fluid type.

An Integrated Numerical and Mineralogical Study of a High Pressure High Temperature Well

Predicting drilling risks in advance is a major challenge in areas that lack drilling experience, and even when information from offset wells is available. Large overpressure was found at TD of an offshore exploratory well drilled mainly through shale. None of the other two previously drilled offset wells in the area had shown any sign of such a high overpressure. This study presents two complementary approaches to gain insight on the overpressure generation mechanisms. The effect of chemical compaction is first evaluated in terms of well cuttings analysis, including sample washing, high-resolution photo catalog, automated mineralogy and X-ray diffraction clay mineralogy analysis. The obtained mineralogical results confirm the presence of the dehydration diagenetic process involving the transformation of smectite to illite. Consequently, a numerical model is presented which combines the effect of mechanical and chemical compaction to predict pore pressure values very close to the overpressure observed during drilling. The model reproduces the depositional history of the lithological column by coupling mechanical and chemical compaction with fluid flow over geological time, and it allows predicting stress, porosity and pore pressure evolution at different geological ages. Calibration and verification of the results of the pore pressure model is done by comparison to drilling experience and logs (post-drill pore pressure profile, geology tops and density/porosity logs).